ICM_GRANULARITY
Alexandre Unger
2024-06-21
Code preparation
Package installation.
Classified by type of package
library(rmarkdown) # Documentation generation
library(officer) # Manipulating Microsoft Word documents
library(flextable) # Creating formatted tables in Microsoft Word documents
library(tableHTML) # Creating tables in HTML format
library(gtsummary) # Creating summary tables
library(here) # Dealing with file path
# ---- dealing with data
library(dplyr) # Data manipulation and transformation
library(tidyr) # Data tidying
library(DataExplorer) # Exploratory data analysis
library(compareGroups) # Comparing groups
library(psych) # Psychological statistics and data manipulation
library(Hmisc) # Miscellaneous functions for data analysis
# ---- Survival analysis
library(poptrend) # Calculating p-trend in survival analysis
library(survival) # Survival analysis
library(survivalAnalysis) # Survival analysis functions
library(survminer) # Drawing survival curves
library(adjustedCurves) # Plotting adjusted survival curves
library(pammtools) # Plotting adjusted survival curves
library(survIDINRI) # Make reclassification and discrimination
library(glmnet) # Regularized regression models
library(olsrr) # Variable selection at its best !
# ---- Graphics
library(ggplot2) # Data visualization
library(mctest) # Multiple hypothesis testing --> assess colinearity between continuous variable
library(corrplot) # Visualizing correlation matrices
library(fmsb) # Creating radar plots
library(concreg) # Plotting adjusted survival curves
library(ggpubr) # Associating the risk table when plotting
# ---- My functions (located in the scrits/functions_R file)
source(here("functions_R","CoxPredictScore.R"))
source(here("functions_R","createSurvivalPlot.R"))
source(here("functions_R","process_data.R"))
source(here("functions_R","extract_model_stats.R"))
source(here("functions_R","forest_function.R"))
source(here("functions_R","uni_multi_function.R")) # function that does the tableData Download (RData).
Using the data.management_ICM_Tradi_V4 results directly saved in RData. * df_all : 6,082 patients * df_LGE : 3,591 patients * df_MACE : 1,500 patients with the informations
load(file = here("data","df_all.RData"))
load(file = here("data","df_LGE.RData"))
load(file = here("data","df_MACE.RData"))Creation of differents files
library(here)
# Get today's date and format it as a string in the required format
date_suffix <- format(Sys.Date(), "%Y-%m-%d")
# Define the directory paths including the date with specified prefixes
tables_date_dir <- here("outputs", "tables", paste0("Tab-", date_suffix))
figures_date_dir <- here("outputs", "figures", paste0("Fig-", date_suffix))
# Check and create the "tables" date-specific directory if it doesn't exist
if (!dir.exists(tables_date_dir)) {
dir.create(tables_date_dir, recursive = TRUE)
message("Directory created: ", tables_date_dir)
} else {
message("Directory already exists: ", tables_date_dir)
}## Directory already exists: /Users/alexandreunger/Documents/PROJECTS/ICM/ICM_tradi/RProject_ICM_tradi/outputs/tables/Tab-2024-06-21# Check and create the "figures" date-specific directory if it doesn't exist
if (!dir.exists(figures_date_dir)) {
dir.create(figures_date_dir, recursive = TRUE)
message("Directory created: ", figures_date_dir)
} else {
message("Directory already exists: ", figures_date_dir)
}## Directory already exists: /Users/alexandreunger/Documents/PROJECTS/ICM/ICM_tradi/RProject_ICM_tradi/outputs/figures/Fig-2024-06-21# Save the directory paths for later use in the script
tables_output_dir <- tables_date_dir
figures_output_dir <- figures_date_dirAll population (N=6,081)
Descriptive
Tab1-descr-all-LGE_presence
Descr_table = createTable(compareGroups(
CMR_LGE_ischemic_presence ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
outcome_revascularisation_90days +
CMR_LVEF + CMR_LVEDV +
CMR_LVESV + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_presence_ischemic_and_midwall +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_midwall_extent_count +
CMR_LGE_midwall_extent_categ +
CMR_LGE_midwall_location_3 +
CMR_LGE_midwall_location_4 +
CMR_LGE_midwall_anterior +
CMR_LGE_midwall_septal +
CMR_LGE_midwall_inferior +
CMR_LGE_midwall_lateral +
CMR_LGE_midwall_apical,
data= df_all,
method = 1, conf.level = 0.995),
hide.no = "No",
show.all=T, show.p.overall = T)
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| [ALL] | No_ischemic_LGE | Presence_of_ischemic_LGE | p.overall | |
|---|---|---|---|---|
| N=6082 | N=2491 | N=3591 | ||
| demo_age | 64.5 (11.8) | 64.2 (11.9) | 64.8 (11.7) | 0.069 |
| demo_gender | 4419 (72.7%) | 1740 (69.9%) | 2679 (74.6%) | <0.001 |
| demo_BMI | 27.7 (5.33) | 27.7 (5.21) | 27.7 (5.41) | 0.893 |
| CV_risk_diabete | 2309 (38.0%) | 863 (34.6%) | 1446 (40.3%) | <0.001 |
| CV_risk_HTA | 3144 (51.7%) | 1240 (49.8%) | 1904 (53.0%) | 0.014 |
| CV_risk_obesity | 1687 (27.7%) | 700 (28.1%) | 987 (27.5%) | 0.618 |
| CV_risk_dyslipidemia | 3005 (49.4%) | 1214 (48.7%) | 1791 (49.9%) | 0.397 |
| CV_risk_Smoking | 1285 (21.1%) | 519 (20.8%) | 766 (21.3%) | 0.664 |
| history_med_MI | 2354 (38.7%) | 967 (38.8%) | 1387 (38.6%) | 0.899 |
| history_coronary_procedure | 5394 (88.7%) | 2227 (89.4%) | 3167 (88.2%) | 0.155 |
| history_interv_PCI | 380 (6.25%) | 123 (4.94%) | 257 (7.16%) | 0.001 |
| history_interv_CABG | 5085 (83.6%) | 2132 (85.6%) | 2953 (82.2%) | 0.001 |
| med_periph_atheroma | 344 (5.66%) | 130 (5.22%) | 214 (5.96%) | 0.241 |
| history_stroke | 186 (3.06%) | 69 (2.77%) | 117 (3.26%) | 0.312 |
| med_pacemaker | 41 (0.67%) | 17 (0.68%) | 24 (0.67%) | 1.000 |
| med_CKD | 121 (1.99%) | 52 (2.09%) | 69 (1.92%) | 0.717 |
| history_hospit_HF | 368 (6.05%) | 140 (5.62%) | 228 (6.35%) | 0.264 |
| history_AFib | 453 (7.45%) | 185 (7.43%) | 268 (7.46%) | 0.997 |
| clini_NYHA | 753 (12.4%) | 293 (11.8%) | 460 (12.8%) | 0.238 |
| clini_cardiac_rythm | 426 (7.00%) | 177 (7.11%) | 249 (6.93%) | 0.836 |
| outcome_revascularisation_90days | 2773 (45.6%) | 0 (0.00%) | 2773 (77.2%) | 0.000 |
| CMR_LVEF | 43.7 (5.70) | 43.9 (5.50) | 43.6 (5.83) | 0.057 |
| CMR_LVEDV | 103 (21.5) | 103 (21.5) | 103 (21.5) | 0.791 |
| CMR_LVESV | 58.6 (17.8) | 58.5 (17.8) | 58.7 (17.7) | 0.674 |
| CMR_LV_mass | 91.0 (19.5) | 91.0 (19.4) | 91.1 (19.5) | 0.823 |
| CMR_RV_dysfunction | 218 (3.58%) | 71 (2.85%) | 147 (4.09%) | 0.013 |
| CMR_LGE_presence_ischemic_and_midwall | 354 (5.82%) | 0 (0.00%) | 354 (9.86%) | <0.001 |
| CMR_LGE_ischemic_presence: | 0.000 | |||
| No_ischemic_LGE | 2491 (41.0%) | 2491 (100%) | 0 (0.00%) | |
| Presence_of_ischemic_LGE | 3591 (59.0%) | 0 (0.00%) | 3591 (100%) | |
| CMR_LGE_ischemic_extent_count | 1.83 (1.96) | 0.00 (0.00) | 3.10 (1.60) | 0.000 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2491 (100%) | 0 (0.00%) | |
| B_1_2_segments | 1392 (22.9%) | 0 (0.00%) | 1392 (38.8%) | |
| C_3_5_segments | 1953 (32.1%) | 0 (0.00%) | 1953 (54.4%) | |
| D_more6_segments | 246 (4.04%) | 0 (0.00%) | 246 (6.85%) | |
| CMR_LGE_ischemic_transmurality: | 0.000 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2491 (100%) | 0 (0.00%) | |
| B_Subendocardial<50% | 1698 (27.9%) | 0 (0.00%) | 1698 (47.3%) | |
| C_Subendocardial≥50% | 1549 (25.5%) | 0 (0.00%) | 1549 (43.1%) | |
| D_Transmural | 344 (5.66%) | 0 (0.00%) | 344 (9.58%) | |
| CMR_LGE_ischemic_location_4: | 0.000 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2491 (100%) | 0 (0.00%) | |
| B_Neither_anterior_nor_septal | 2662 (43.8%) | 0 (0.00%) | 2662 (74.1%) | |
| C_Anterior_without_septal | 422 (6.94%) | 0 (0.00%) | 422 (11.8%) | |
| D_Septal | 507 (8.34%) | 0 (0.00%) | 507 (14.1%) | |
| CMR_LGE_ischemic_anterior | 786 (12.9%) | 0 (0.00%) | 786 (21.9%) | <0.001 |
| CMR_LGE_ischemic_septal | 507 (8.34%) | 0 (0.00%) | 507 (14.1%) | <0.001 |
| CMR_LGE_ischemic_inferior | 1214 (20.0%) | 0 (0.00%) | 1214 (33.8%) | <0.001 |
| CMR_LGE_ischemic_lateral | 1195 (19.6%) | 0 (0.00%) | 1195 (33.3%) | <0.001 |
| CMR_LGE_ischemic_Apical | 1168 (19.2%) | 0 (0.00%) | 1168 (32.5%) | <0.001 |
| CMR_LGE_midwall_presence: | <0.001 | |||
| A_No_midwall_LGE | 5380 (88.5%) | 2143 (86.0%) | 3237 (90.1%) | |
| B_Presence_of_midwall_LGE | 702 (11.5%) | 348 (14.0%) | 354 (9.86%) | |
| CMR_LGE_midwall_extent_count | 0.13 (0.39) | 0.15 (0.39) | 0.12 (0.38) | 0.001 |
| CMR_LGE_midwall_extent_categ: | <0.001 | |||
| None | 5380 (88.5%) | 2143 (86.0%) | 3237 (90.1%) | |
| Low_=1 | 610 (10.0%) | 321 (12.9%) | 289 (8.05%) | |
| High_>1 | 92 (1.51%) | 27 (1.08%) | 65 (1.81%) | |
| CMR_LGE_midwall_location_3: | <0.001 | |||
| No_midwall_LGE | 5380 (88.5%) | 2143 (86.0%) | 3237 (90.1%) | |
| Midwall_LGE_not_at_risk | 471 (7.74%) | 240 (9.63%) | 231 (6.43%) | |
| At_risk_midwall_LGE_(septal_and/or_lateral) | 231 (3.80%) | 108 (4.34%) | 123 (3.43%) | |
| CMR_LGE_midwall_location_4: | <0.001 | |||
| No_midwall_LGE | 5380 (88.5%) | 2143 (86.0%) | 3237 (90.1%) | |
| Midwall_LGE_not_at_risk | 471 (7.74%) | 240 (9.63%) | 231 (6.43%) | |
| Lateral_midwall_LGE | 118 (1.94%) | 53 (2.13%) | 65 (1.81%) | |
| Septal_Midwall_LGE | 113 (1.86%) | 55 (2.21%) | 58 (1.62%) | |
| CMR_LGE_midwall_anterior | 140 (2.30%) | 41 (1.65%) | 99 (2.76%) | 0.006 |
| CMR_LGE_midwall_septal | 113 (1.86%) | 55 (2.21%) | 58 (1.62%) | 0.113 |
| CMR_LGE_midwall_inferior | 212 (3.49%) | 112 (4.50%) | 100 (2.78%) | <0.001 |
| CMR_LGE_midwall_lateral | 124 (2.04%) | 54 (2.17%) | 70 (1.95%) | 0.617 |
| CMR_LGE_midwall_apical | 146 (2.40%) | 91 (3.65%) | 55 (1.53%) | <0.001 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab1-descr-all-LGE_presence-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab2-descr-all-center
Descr_table = createTable(compareGroups(
demo_center ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
outcome_revascularisation_90days +
CMR_LVEF + CMR_LVEDV +
CMR_LVESV + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_presence_ischemic_and_midwall +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_midwall_extent_count +
CMR_LGE_midwall_extent_categ +
CMR_LGE_midwall_location_3 +
CMR_LGE_midwall_location_4 +
CMR_LGE_midwall_anterior +
CMR_LGE_midwall_septal +
CMR_LGE_midwall_inferior +
CMR_LGE_midwall_lateral +
CMR_LGE_midwall_apical,
data= df_all,
method = 1, conf.level = 0.995),
hide.no = "No",
show.all=T, show.p.overall = T)
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| [ALL] | ICPS | Lariboisiere | p.overall | |
|---|---|---|---|---|
| N=6082 | N=5214 | N=868 | ||
| demo_age | 64.5 (11.8) | 64.4 (11.9) | 65.3 (11.4) | 0.026 |
| demo_gender | 4419 (72.7%) | 3738 (71.7%) | 681 (78.5%) | <0.001 |
| demo_BMI | 27.7 (5.33) | 27.7 (5.34) | 27.8 (5.24) | 0.658 |
| CV_risk_diabete | 2309 (38.0%) | 1850 (35.5%) | 459 (52.9%) | <0.001 |
| CV_risk_HTA | 3144 (51.7%) | 2698 (51.7%) | 446 (51.4%) | 0.872 |
| CV_risk_obesity | 1687 (27.7%) | 1452 (27.8%) | 235 (27.1%) | 0.667 |
| CV_risk_dyslipidemia | 3005 (49.4%) | 2571 (49.3%) | 434 (50.0%) | 0.734 |
| CV_risk_Smoking | 1285 (21.1%) | 1092 (20.9%) | 193 (22.2%) | 0.413 |
| history_med_MI | 2354 (38.7%) | 1929 (37.0%) | 425 (49.0%) | <0.001 |
| history_coronary_procedure | 5394 (88.7%) | 4657 (89.3%) | 737 (84.9%) | <0.001 |
| history_interv_PCI | 380 (6.25%) | 278 (5.33%) | 102 (11.8%) | <0.001 |
| history_interv_CABG | 5085 (83.6%) | 4439 (85.1%) | 646 (74.4%) | <0.001 |
| med_periph_atheroma | 344 (5.66%) | 294 (5.64%) | 50 (5.76%) | 0.949 |
| history_stroke | 186 (3.06%) | 160 (3.07%) | 26 (3.00%) | 0.992 |
| med_pacemaker | 41 (0.67%) | 37 (0.71%) | 4 (0.46%) | 0.545 |
| med_CKD | 121 (1.99%) | 111 (2.13%) | 10 (1.15%) | 0.076 |
| history_hospit_HF | 368 (6.05%) | 306 (5.87%) | 62 (7.14%) | 0.167 |
| history_AFib | 453 (7.45%) | 395 (7.58%) | 58 (6.68%) | 0.390 |
| clini_NYHA | 753 (12.4%) | 645 (12.4%) | 108 (12.4%) | 0.997 |
| clini_cardiac_rythm | 426 (7.00%) | 367 (7.04%) | 59 (6.80%) | 0.852 |
| outcome_revascularisation_90days | 2773 (45.6%) | 2235 (42.9%) | 538 (62.0%) | <0.001 |
| CMR_LVEF | 43.7 (5.70) | 43.8 (5.53) | 43.1 (6.61) | 0.003 |
| CMR_LVEDV | 103 (21.5) | 103 (20.9) | 104 (25.0) | 0.189 |
| CMR_LVESV | 58.6 (17.8) | 58.4 (17.3) | 59.9 (20.3) | 0.035 |
| CMR_LV_mass | 91.0 (19.5) | 91.0 (19.4) | 91.6 (19.8) | 0.399 |
| CMR_RV_dysfunction | 218 (3.58%) | 174 (3.34%) | 44 (5.07%) | 0.015 |
| CMR_LGE_presence_ischemic_and_midwall | 354 (5.82%) | 307 (5.89%) | 47 (5.41%) | 0.636 |
| CMR_LGE_ischemic_presence: | <0.001 | |||
| No_ischemic_LGE | 2491 (41.0%) | 2314 (44.4%) | 177 (20.4%) | |
| Presence_of_ischemic_LGE | 3591 (59.0%) | 2900 (55.6%) | 691 (79.6%) | |
| CMR_LGE_ischemic_extent_count | 1.83 (1.96) | 1.74 (1.96) | 2.37 (1.85) | <0.001 |
| CMR_LGE_ischemic_extent_categ: | <0.001 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2314 (44.4%) | 177 (20.4%) | |
| B_1_2_segments | 1392 (22.9%) | 1102 (21.1%) | 290 (33.4%) | |
| C_3_5_segments | 1953 (32.1%) | 1596 (30.6%) | 357 (41.1%) | |
| D_more6_segments | 246 (4.04%) | 202 (3.87%) | 44 (5.07%) | |
| CMR_LGE_ischemic_transmurality: | <0.001 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2314 (44.4%) | 177 (20.4%) | |
| B_Subendocardial<50% | 1698 (27.9%) | 1355 (26.0%) | 343 (39.5%) | |
| C_Subendocardial≥50% | 1549 (25.5%) | 1268 (24.3%) | 281 (32.4%) | |
| D_Transmural | 344 (5.66%) | 277 (5.31%) | 67 (7.72%) | |
| CMR_LGE_ischemic_location_4: | <0.001 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2314 (44.4%) | 177 (20.4%) | |
| B_Neither_anterior_nor_septal | 2662 (43.8%) | 2136 (41.0%) | 526 (60.6%) | |
| C_Anterior_without_septal | 422 (6.94%) | 351 (6.73%) | 71 (8.18%) | |
| D_Septal | 507 (8.34%) | 413 (7.92%) | 94 (10.8%) | |
| CMR_LGE_ischemic_anterior | 786 (12.9%) | 651 (12.5%) | 135 (15.6%) | 0.015 |
| CMR_LGE_ischemic_septal | 507 (8.34%) | 413 (7.92%) | 94 (10.8%) | 0.005 |
| CMR_LGE_ischemic_inferior | 1214 (20.0%) | 970 (18.6%) | 244 (28.1%) | <0.001 |
| CMR_LGE_ischemic_lateral | 1195 (19.6%) | 974 (18.7%) | 221 (25.5%) | <0.001 |
| CMR_LGE_ischemic_Apical | 1168 (19.2%) | 960 (18.4%) | 208 (24.0%) | <0.001 |
| CMR_LGE_midwall_presence: | <0.001 | |||
| A_No_midwall_LGE | 5380 (88.5%) | 4581 (87.9%) | 799 (92.1%) | |
| B_Presence_of_midwall_LGE | 702 (11.5%) | 633 (12.1%) | 69 (7.95%) | |
| CMR_LGE_midwall_extent_count | 0.13 (0.39) | 0.14 (0.39) | 0.10 (0.35) | 0.001 |
| CMR_LGE_midwall_extent_categ: | 0.001 | |||
| None | 5380 (88.5%) | 4581 (87.9%) | 799 (92.1%) | |
| Low_=1 | 610 (10.0%) | 553 (10.6%) | 57 (6.57%) | |
| High_>1 | 92 (1.51%) | 80 (1.53%) | 12 (1.38%) | |
| CMR_LGE_midwall_location_3: | 0.002 | |||
| No_midwall_LGE | 5380 (88.5%) | 4581 (87.9%) | 799 (92.1%) | |
| Midwall_LGE_not_at_risk | 471 (7.74%) | 426 (8.17%) | 45 (5.18%) | |
| At_risk_midwall_LGE_(septal_and/or_lateral) | 231 (3.80%) | 207 (3.97%) | 24 (2.76%) | |
| CMR_LGE_midwall_location_4: | 0.004 | |||
| No_midwall_LGE | 5380 (88.5%) | 4581 (87.9%) | 799 (92.1%) | |
| Midwall_LGE_not_at_risk | 471 (7.74%) | 426 (8.17%) | 45 (5.18%) | |
| Lateral_midwall_LGE | 118 (1.94%) | 104 (1.99%) | 14 (1.61%) | |
| Septal_Midwall_LGE | 113 (1.86%) | 103 (1.98%) | 10 (1.15%) | |
| CMR_LGE_midwall_anterior | 140 (2.30%) | 132 (2.53%) | 8 (0.92%) | 0.005 |
| CMR_LGE_midwall_septal | 113 (1.86%) | 103 (1.98%) | 10 (1.15%) | 0.127 |
| CMR_LGE_midwall_inferior | 212 (3.49%) | 191 (3.66%) | 21 (2.42%) | 0.080 |
| CMR_LGE_midwall_lateral | 124 (2.04%) | 110 (2.11%) | 14 (1.61%) | 0.407 |
| CMR_LGE_midwall_apical | 146 (2.40%) | 127 (2.44%) | 19 (2.19%) | 0.749 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab2-descr-all-center-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab3-descr-all-death
df_selected <- df_all %>% mutate(
outcome_death = as.factor(outcome_death),
outcome_MACE = as.factor(outcome_MACE ),
outcome_MACE_CV_death = as.factor(outcome_MACE_CV_death),
outcome_MACE_NonFatal_MI = as.factor(outcome_MACE_NonFatal_MI)
)
Descr_table = createTable(compareGroups(
outcome_death ~
outcome_revascularisation_90days + outcome_death + outcome_MACE + outcome_MACE_CV_death + outcome_MACE_NonFatal_MI +
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
outcome_revascularisation_90days +
CMR_LVEF + CMR_LVEDV +
CMR_LVESV + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_presence_ischemic_and_midwall +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_midwall_extent_count +
CMR_LGE_midwall_extent_categ +
CMR_LGE_midwall_location_3 +
CMR_LGE_midwall_location_4 +
CMR_LGE_midwall_anterior +
CMR_LGE_midwall_septal +
CMR_LGE_midwall_inferior +
CMR_LGE_midwall_lateral +
CMR_LGE_midwall_apical,
data= df_selected,
method = 1, conf.level = 0.995),
hide.no = "No",
show.all=T, show.p.overall = T)
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| [ALL] | 0 | 1 | p.overall | |
|---|---|---|---|---|
| N=6082 | N=5430 | N=652 | ||
| outcome_revascularisation_90days | 2773 (45.6%) | 2525 (46.5%) | 248 (38.0%) | <0.001 |
| outcome_death: | 0.000 | |||
| 0 | 5430 (89.3%) | 5430 (100%) | 0 (0.00%) | |
| 1 | 652 (10.7%) | 0 (0.00%) | 652 (100%) | |
| outcome_MACE: | <0.001 | |||
| 0 | 1227 (81.8%) | 1207 (94.8%) | 20 (8.81%) | |
| 1 | 273 (18.2%) | 66 (5.18%) | 207 (91.2%) | |
| outcome_MACE_CV_death: | <0.001 | |||
| 0 | 1286 (86.5%) | 1269 (100%) | 17 (7.83%) | |
| 1 | 200 (13.5%) | 0 (0.00%) | 200 (92.2%) | |
| outcome_MACE_NonFatal_MI: | 0.302 | |||
| 0 | 1414 (95.2%) | 1204 (94.9%) | 210 (96.8%) | |
| 1 | 72 (4.85%) | 65 (5.12%) | 7 (3.23%) | |
| demo_age | 64.5 (11.8) | 64.1 (11.8) | 68.3 (11.3) | <0.001 |
| demo_gender | 4419 (72.7%) | 3843 (70.8%) | 576 (88.3%) | <0.001 |
| demo_BMI | 27.7 (5.33) | 27.7 (5.30) | 27.8 (5.57) | 0.553 |
| CV_risk_diabete | 2309 (38.0%) | 1945 (35.8%) | 364 (55.8%) | <0.001 |
| CV_risk_HTA | 3144 (51.7%) | 2817 (51.9%) | 327 (50.2%) | 0.429 |
| CV_risk_obesity | 1687 (27.7%) | 1509 (27.8%) | 178 (27.3%) | 0.828 |
| CV_risk_dyslipidemia | 3005 (49.4%) | 2677 (49.3%) | 328 (50.3%) | 0.657 |
| CV_risk_Smoking | 1285 (21.1%) | 1137 (20.9%) | 148 (22.7%) | 0.322 |
| history_med_MI | 2354 (38.7%) | 2090 (38.5%) | 264 (40.5%) | 0.343 |
| history_coronary_procedure | 5394 (88.7%) | 4817 (88.7%) | 577 (88.5%) | 0.922 |
| history_interv_PCI | 380 (6.25%) | 310 (5.71%) | 70 (10.7%) | <0.001 |
| history_interv_CABG | 5085 (83.6%) | 4566 (84.1%) | 519 (79.6%) | 0.004 |
| med_periph_atheroma | 344 (5.66%) | 276 (5.08%) | 68 (10.4%) | <0.001 |
| history_stroke | 186 (3.06%) | 150 (2.76%) | 36 (5.52%) | <0.001 |
| med_pacemaker | 41 (0.67%) | 38 (0.70%) | 3 (0.46%) | 0.618 |
| med_CKD | 121 (1.99%) | 84 (1.55%) | 37 (5.67%) | <0.001 |
| history_hospit_HF | 368 (6.05%) | 286 (5.27%) | 82 (12.6%) | <0.001 |
| history_AFib | 453 (7.45%) | 382 (7.03%) | 71 (10.9%) | 0.001 |
| clini_NYHA | 753 (12.4%) | 668 (12.3%) | 85 (13.0%) | 0.635 |
| clini_cardiac_rythm | 426 (7.00%) | 368 (6.78%) | 58 (8.90%) | 0.055 |
| outcome_revascularisation_90days | 2773 (45.6%) | 2525 (46.5%) | 248 (38.0%) | <0.001 |
| CMR_LVEF | 43.7 (5.70) | 43.9 (5.58) | 42.2 (6.38) | <0.001 |
| CMR_LVEDV | 103 (21.5) | 103 (21.2) | 106 (24.0) | 0.002 |
| CMR_LVESV | 58.6 (17.8) | 58.2 (17.5) | 61.9 (19.6) | <0.001 |
| CMR_LV_mass | 91.0 (19.5) | 91.1 (19.5) | 90.9 (19.5) | 0.870 |
| CMR_RV_dysfunction | 218 (3.58%) | 153 (2.82%) | 65 (9.97%) | <0.001 |
| CMR_LGE_presence_ischemic_and_midwall | 354 (5.82%) | 223 (4.11%) | 131 (20.1%) | <0.001 |
| CMR_LGE_ischemic_presence: | <0.001 | |||
| No_ischemic_LGE | 2491 (41.0%) | 2388 (44.0%) | 103 (15.8%) | |
| Presence_of_ischemic_LGE | 3591 (59.0%) | 3042 (56.0%) | 549 (84.2%) | |
| CMR_LGE_ischemic_extent_count | 1.83 (1.96) | 1.53 (1.66) | 4.30 (2.44) | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2388 (44.0%) | 103 (15.8%) | |
| B_1_2_segments | 1392 (22.9%) | 1369 (25.2%) | 23 (3.53%) | |
| C_3_5_segments | 1953 (32.1%) | 1659 (30.6%) | 294 (45.1%) | |
| D_more6_segments | 246 (4.04%) | 14 (0.26%) | 232 (35.6%) | |
| CMR_LGE_ischemic_transmurality: | <0.001 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2388 (44.0%) | 103 (15.8%) | |
| B_Subendocardial<50% | 1698 (27.9%) | 1619 (29.8%) | 79 (12.1%) | |
| C_Subendocardial≥50% | 1549 (25.5%) | 1290 (23.8%) | 259 (39.7%) | |
| D_Transmural | 344 (5.66%) | 133 (2.45%) | 211 (32.4%) | |
| CMR_LGE_ischemic_location_4: | 0.000 | |||
| A_No_ischemic_LGE | 2491 (41.0%) | 2388 (44.0%) | 103 (15.8%) | |
| B_Neither_anterior_nor_septal | 2662 (43.8%) | 2563 (47.2%) | 99 (15.2%) | |
| C_Anterior_without_septal | 422 (6.94%) | 317 (5.84%) | 105 (16.1%) | |
| D_Septal | 507 (8.34%) | 162 (2.98%) | 345 (52.9%) | |
| CMR_LGE_ischemic_anterior | 786 (12.9%) | 384 (7.07%) | 402 (61.7%) | 0.000 |
| CMR_LGE_ischemic_septal | 507 (8.34%) | 162 (2.98%) | 345 (52.9%) | 0.000 |
| CMR_LGE_ischemic_inferior | 1214 (20.0%) | 1097 (20.2%) | 117 (17.9%) | 0.190 |
| CMR_LGE_ischemic_lateral | 1195 (19.6%) | 966 (17.8%) | 229 (35.1%) | <0.001 |
| CMR_LGE_ischemic_Apical | 1168 (19.2%) | 875 (16.1%) | 293 (44.9%) | <0.001 |
| CMR_LGE_midwall_presence: | <0.001 | |||
| A_No_midwall_LGE | 5380 (88.5%) | 4901 (90.3%) | 479 (73.5%) | |
| B_Presence_of_midwall_LGE | 702 (11.5%) | 529 (9.74%) | 173 (26.5%) | |
| CMR_LGE_midwall_extent_count | 0.13 (0.39) | 0.10 (0.31) | 0.39 (0.71) | <0.001 |
| CMR_LGE_midwall_extent_categ: | <0.001 | |||
| None | 5380 (88.5%) | 4901 (90.3%) | 479 (73.5%) | |
| Low_=1 | 610 (10.0%) | 510 (9.39%) | 100 (15.3%) | |
| High_>1 | 92 (1.51%) | 19 (0.35%) | 73 (11.2%) | |
| CMR_LGE_midwall_location_3: | <0.001 | |||
| No_midwall_LGE | 5380 (88.5%) | 4901 (90.3%) | 479 (73.5%) | |
| Midwall_LGE_not_at_risk | 471 (7.74%) | 406 (7.48%) | 65 (9.97%) | |
| At_risk_midwall_LGE_(septal_and/or_lateral) | 231 (3.80%) | 123 (2.27%) | 108 (16.6%) | |
| CMR_LGE_midwall_location_4: | <0.001 | |||
| No_midwall_LGE | 5380 (88.5%) | 4901 (90.3%) | 479 (73.5%) | |
| Midwall_LGE_not_at_risk | 471 (7.74%) | 406 (7.48%) | 65 (9.97%) | |
| Lateral_midwall_LGE | 118 (1.94%) | 64 (1.18%) | 54 (8.28%) | |
| Septal_Midwall_LGE | 113 (1.86%) | 59 (1.09%) | 54 (8.28%) | |
| CMR_LGE_midwall_anterior | 140 (2.30%) | 113 (2.08%) | 27 (4.14%) | 0.001 |
| CMR_LGE_midwall_septal | 113 (1.86%) | 59 (1.09%) | 54 (8.28%) | <0.001 |
| CMR_LGE_midwall_inferior | 212 (3.49%) | 166 (3.06%) | 46 (7.06%) | <0.001 |
| CMR_LGE_midwall_lateral | 124 (2.04%) | 64 (1.18%) | 60 (9.20%) | <0.001 |
| CMR_LGE_midwall_apical | 146 (2.40%) | 132 (2.43%) | 14 (2.15%) | 0.755 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab3-descr-all-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Survival
Tab4-univ-all-death
df_selected <- df_all %>%
mutate(
surv.event = Surv(
time = outcome_FU_time_death,
event = outcome_death
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
outcome_revascularisation_90days +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_presence_ischemic_and_midwall +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_midwall_extent_count +
CMR_LGE_midwall_extent_categ +
CMR_LGE_midwall_location_3 +
CMR_LGE_midwall_location_4 +
CMR_LGE_midwall_anterior +
CMR_LGE_midwall_septal +
CMR_LGE_midwall_inferior +
CMR_LGE_midwall_lateral +
CMR_LGE_midwall_apical,
data= df_selected,
method = 1, conf.level = 0.995),
hide.no = "No",
show.ratio =T, show.p.ratio = T)
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=5430 | N=652 | ||||
| demo_age | 64.1 (11.8) | 68.3 (11.3) | 1.03 [1.02;1.04] | <0.001 | <0.001 |
| demo_gender | 3843 (70.8%) | 576 (88.3%) | 2.83 [2.01;3.98] | <0.001 | 0.000 |
| demo_BMI | 27.7 (5.30) | 27.8 (5.57) | 1.01 [0.98;1.03] | 0.490 | 0.490 |
| CV_risk_diabete | 1945 (35.8%) | 364 (55.8%) | 2.09 [1.68;2.61] | <0.001 | 0.000 |
| CV_risk_HTA | 2817 (51.9%) | 327 (50.2%) | 0.92 [0.74;1.14] | 0.277 | 0.276 |
| CV_risk_obesity | 1509 (27.8%) | 178 (27.3%) | 0.98 [0.76;1.25] | 0.804 | 0.805 |
| CV_risk_dyslipidemia | 2677 (49.3%) | 328 (50.3%) | 1.04 [0.83;1.29] | 0.637 | 0.634 |
| CV_risk_Smoking | 1137 (20.9%) | 148 (22.7%) | 1.08 [0.83;1.41] | 0.401 | 0.402 |
| history_med_MI | 2090 (38.5%) | 264 (40.5%) | 1.08 [0.86;1.35] | 0.342 | 0.341 |
| history_coronary_procedure | 4817 (88.7%) | 577 (88.5%) | 0.98 [0.70;1.39] | 0.878 | 0.879 |
| history_interv_PCI | 310 (5.71%) | 70 (10.7%) | 1.74 [1.21;2.49] | <0.001 | <0.001 |
| history_interv_CABG | 4566 (84.1%) | 519 (79.6%) | 0.78 [0.59;1.03] | 0.011 | 0.011 |
| med_periph_atheroma | 276 (5.08%) | 68 (10.4%) | 1.97 [1.38;2.82] | <0.001 | <0.001 |
| history_stroke | 150 (2.76%) | 36 (5.52%) | 1.92 [1.18;3.11] | <0.001 | <0.001 |
| med_pacemaker | 38 (0.70%) | 3 (0.46%) | 0.71 [0.14;3.61] | 0.556 | 0.555 |
| med_CKD | 84 (1.55%) | 37 (5.67%) | 3.98 [2.48;6.41] | <0.001 | 0.000 |
| history_hospit_HF | 286 (5.27%) | 82 (12.6%) | 2.32 [1.66;3.23] | <0.001 | <0.001 |
| history_AFib | 382 (7.03%) | 71 (10.9%) | 1.60 [1.12;2.28] | <0.001 | <0.001 |
| clini_NYHA | 668 (12.3%) | 85 (13.0%) | 1.02 [0.73;1.41] | 0.885 | 0.884 |
| clini_cardiac_rythm | 368 (6.78%) | 58 (8.90%) | 1.34 [0.91;1.97] | 0.036 | 0.034 |
| outcome_revascularisation_90days | 2525 (46.5%) | 248 (38.0%) | 0.73 [0.58;0.91] | <0.001 | <0.001 |
| CMR_LVEF_5 | 8.79 (1.12) | 8.43 (1.28) | 0.82 [0.75;0.89] | <0.001 | <0.001 |
| CMR_LVEDV_5 | 10.3 (2.12) | 10.6 (2.40) | 1.05 [1.01;1.10] | 0.002 | 0.002 |
| CMR_LVESV_5 | 5.82 (1.75) | 6.19 (1.96) | 1.09 [1.03;1.14] | <0.001 | <0.001 |
| CMR_LV_mass | 91.1 (19.5) | 90.9 (19.5) | 1.00 [0.99;1.01] | 0.772 | 0.772 |
| CMR_RV_dysfunction | 153 (2.82%) | 65 (9.97%) | 3.32 [2.30;4.79] | <0.001 | 0.000 |
| CMR_LGE_presence_ischemic_and_midwall | 223 (4.11%) | 131 (20.1%) | 4.44 [3.37;5.85] | <0.001 | 0.000 |
| CMR_LGE_ischemic_presence: | 0.000 | ||||
| No_ischemic_LGE | 2388 (44.0%) | 103 (15.8%) | Ref. | Ref. | |
| Presence_of_ischemic_LGE | 3042 (56.0%) | 549 (84.2%) | 3.76 [2.78;5.08] | <0.001 | |
| CMR_LGE_ischemic_extent_count | 1.53 (1.66) | 4.30 (2.44) | 1.70 [1.62;1.79] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| A_No_ischemic_LGE | 2388 (44.0%) | 103 (15.8%) | Ref. | Ref. | |
| B_1_2_segments | 1369 (25.2%) | 23 (3.53%) | 0.41 [0.21;0.78] | <0.001 | |
| C_3_5_segments | 1659 (30.6%) | 294 (45.1%) | 3.64 [2.64;5.02] | <0.001 | |
| D_more6_segments | 14 (0.26%) | 232 (35.6%) | 27.7 [19.9;38.7] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| A_No_ischemic_LGE | 2388 (44.0%) | 103 (15.8%) | Ref. | Ref. | |
| B_Subendocardial<50% | 1619 (29.8%) | 79 (12.1%) | 1.14 [0.75;1.73] | 0.386 | |
| C_Subendocardial≥50% | 1290 (23.8%) | 259 (39.7%) | 4.06 [2.93;5.63] | <0.001 | |
| D_Transmural | 133 (2.45%) | 211 (32.4%) | 16.7 [11.9;23.4] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| A_No_ischemic_LGE | 2388 (44.0%) | 103 (15.8%) | Ref. | Ref. | |
| B_Neither_anterior_nor_septal | 2563 (47.2%) | 99 (15.2%) | 0.89 [0.60;1.32] | 0.395 | |
| C_Anterior_without_septal | 317 (5.84%) | 105 (16.1%) | 6.40 [4.34;9.45] | <0.001 | |
| D_Septal | 162 (2.98%) | 345 (52.9%) | 19.4 [14.1;26.6] | <0.001 | |
| CMR_LGE_ischemic_anterior | 384 (7.07%) | 402 (61.7%) | 12.4 [9.87;15.5] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 162 (2.98%) | 345 (52.9%) | 14.6 [11.7;18.2] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 1097 (20.2%) | 117 (17.9%) | 0.88 [0.66;1.17] | 0.218 | 0.216 |
| CMR_LGE_ischemic_lateral | 966 (17.8%) | 229 (35.1%) | 2.20 [1.75;2.77] | <0.001 | 0.000 |
| CMR_LGE_ischemic_Apical | 875 (16.1%) | 293 (44.9%) | 3.54 [2.84;4.42] | <0.001 | 0.000 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 4901 (90.3%) | 479 (73.5%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 529 (9.74%) | 173 (26.5%) | 2.85 [2.22;3.66] | <0.001 | |
| CMR_LGE_midwall_extent_count | 0.10 (0.31) | 0.39 (0.71) | 2.63 [2.24;3.10] | <0.001 | <0.001 |
| CMR_LGE_midwall_extent_categ: | 0.000 | ||||
| None | 4901 (90.3%) | 479 (73.5%) | Ref. | Ref. | |
| Low_=1 | 510 (9.39%) | 100 (15.3%) | 1.88 [1.38;2.56] | <0.001 | |
| High_>1 | 19 (0.35%) | 73 (11.2%) | 10.3 [7.21;14.7] | <0.001 | |
| CMR_LGE_midwall_location_3: | 0.000 | ||||
| No_midwall_LGE | 4901 (90.3%) | 479 (73.5%) | Ref. | Ref. | |
| Midwall_LGE_not_at_risk | 406 (7.48%) | 65 (9.97%) | 1.53 [1.06;2.22] | 0.001 | |
| At_risk_midwall_LGE_(septal_and/or_lateral) | 123 (2.27%) | 108 (16.6%) | 5.94 [4.39;8.02] | <0.001 | |
| CMR_LGE_midwall_location_4: | 0.000 | ||||
| No_midwall_LGE | 4901 (90.3%) | 479 (73.5%) | Ref. | Ref. | |
| Midwall_LGE_not_at_risk | 406 (7.48%) | 65 (9.97%) | 1.53 [1.05;2.22] | 0.001 | |
| Lateral_midwall_LGE | 64 (1.18%) | 54 (8.28%) | 6.03 [4.03;9.02] | <0.001 | |
| Septal_Midwall_LGE | 59 (1.09%) | 54 (8.28%) | 5.84 [3.89;8.79] | <0.001 | |
| CMR_LGE_midwall_anterior | 113 (2.08%) | 27 (4.14%) | 1.62 [0.93;2.83] | 0.015 | 0.012 |
| CMR_LGE_midwall_septal | 59 (1.09%) | 54 (8.28%) | 5.16 [3.45;7.74] | <0.001 | 0.000 |
| CMR_LGE_midwall_inferior | 166 (3.06%) | 46 (7.06%) | 2.46 [1.60;3.78] | <0.001 | <0.001 |
| CMR_LGE_midwall_lateral | 64 (1.18%) | 60 (9.20%) | 5.75 [3.93;8.42] | <0.001 | 0.000 |
| CMR_LGE_midwall_apical | 132 (2.43%) | 14 (2.15%) | 0.79 [0.37;1.69] | 0.382 | 0.370 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab4-univ-all-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab5-multi_tradi-all-death
df <- df_all %>% mutate(
event = outcome_death,
time = outcome_FU_time_death,
CMR_LVEF_5 = CMR_LVEF/5
) %>% select(
event, time,
demo_age, demo_gender, demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF, history_AFib, med_CKD, history_med_MI , CMR_LVEF_5, CMR_LGE_ischemic_presence) %>% droplevels()
#### TABLE
## Model 1
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LVEF_5))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 1", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.03 ( 0.02 - 0.04 ) | 1.03 ( 1.02 - 1.04 ) | <0.001 |
| demo_genderYes | 1.00 ( 0.66 - 1.35 ) | 2.73 ( 1.93 - 3.86 ) | <0.001 |
| demo_BMI | 0.00 ( -0.02 - 0.03 ) | 1.00 ( 0.98 - 1.03 ) | 0.855 |
| CV_risk_diabeteYes | 0.64 ( 0.41 - 0.87 ) | 1.91 ( 1.51 - 2.40 ) | <0.001 |
| CV_risk_SmokingYes | 0.25 ( -0.03 - 0.52 ) | 1.28 ( 0.97 - 1.69 ) | 0.011 |
| CV_risk_dyslipidemiaYes | -0.08 ( -0.30 - 0.15 ) | 0.93 ( 0.74 - 1.16 ) | 0.338 |
| history_hospit_HFYes | 0.51 ( 0.15 - 0.86 ) | 1.66 ( 1.16 - 2.37 ) | <0.001 |
| history_AFibYes | 0.03 ( -0.33 - 0.40 ) | 1.04 ( 0.72 - 1.49 ) | 0.791 |
| med_CKDYes | 0.98 ( 0.49 - 1.47 ) | 2.66 ( 1.62 - 4.35 ) | <0.001 |
| history_med_MIYes | 0.03 ( -0.20 - 0.26 ) | 1.03 ( 0.82 - 1.30 ) | 0.705 |
| CMR_LVEF_5 | -0.10 ( -0.19 - -0.01 ) | 0.90 ( 0.83 - 0.99 ) | 0.001 |
## Model 2
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 2", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.03 ( 0.02 - 0.04 ) | 1.03 ( 1.02 - 1.04 ) | <0.001 |
| demo_genderYes | 0.96 ( 0.61 - 1.31 ) | 2.61 ( 1.84 - 3.69 ) | <0.001 |
| demo_BMI | 0.00 ( -0.02 - 0.03 ) | 1.00 ( 0.98 - 1.03 ) | 0.614 |
| CV_risk_diabeteYes | 0.58 ( 0.35 - 0.81 ) | 1.79 ( 1.42 - 2.26 ) | <0.001 |
| CV_risk_SmokingYes | 0.25 ( -0.03 - 0.52 ) | 1.28 ( 0.97 - 1.69 ) | 0.012 |
| CV_risk_dyslipidemiaYes | -0.07 ( -0.29 - 0.16 ) | 0.94 ( 0.75 - 1.17 ) | 0.411 |
| history_hospit_HFYes | 0.49 ( 0.13 - 0.84 ) | 1.63 ( 1.14 - 2.32 ) | <0.001 |
| history_AFibYes | 0.04 ( -0.33 - 0.40 ) | 1.04 ( 0.72 - 1.50 ) | 0.762 |
| med_CKDYes | 0.98 ( 0.49 - 1.47 ) | 2.67 ( 1.63 - 4.37 ) | <0.001 |
| history_med_MIYes | 0.05 ( -0.18 - 0.28 ) | 1.05 ( 0.83 - 1.32 ) | 0.560 |
| CMR_LVEF_5 | -0.09 ( -0.18 - -0.00 ) | 0.92 ( 0.84 - 1.00 ) | 0.005 |
| CMR_LGE_ischemic_presencePresence_of_ischemic_LGE | 1.24 ( 0.94 - 1.54 ) | 3.45 ( 2.55 - 4.67 ) | <0.001 |
# Table uni multi
table <- uni_multi_function(
df = df,
event = "event",
time = "time",
var_base = c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5"),
var_added = c("CMR_LGE_ischemic_presence"))
# export in html
knitr::kable(table, caption = "Model 2 - LGE presence - All population", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Variable | Univariate_Analysis | model_multi_1 | model_multi_2 |
|---|---|---|---|
| demo_age | 1.03 (1.02-1.04), p=0.000 | 1.03 (1.02-1.04), p=0.000 | 1.03 (1.02-1.04), p=0.000 |
| demo_genderYes | 2.83 (2.01-3.98), p=0.000 | 2.73 (1.93-3.86), p=0.000 | 2.61 (1.84-3.69), p=0.000 |
| demo_BMI | 1.01 (0.98-1.03), p=0.490 | 1.00 (0.98-1.03), p=0.855 | 1.00 (0.98-1.03), p=0.614 |
| CV_risk_diabeteYes | 2.09 (1.68-2.61), p=0.000 | 1.91 (1.51-2.40), p=0.000 | 1.79 (1.42-2.26), p=0.000 |
| CV_risk_SmokingYes | 1.08 (0.83-1.41), p=0.401 | 1.28 (0.97-1.69), p=0.011 | 1.28 (0.97-1.69), p=0.012 |
| CV_risk_dyslipidemiaYes | 1.04 (0.83-1.29), p=0.637 | 0.93 (0.74-1.16), p=0.338 | 0.94 (0.75-1.17), p=0.411 |
| history_hospit_HFYes | 2.32 (1.66-3.23), p=0.000 | 1.66 (1.16-2.37), p=0.000 | 1.63 (1.14-2.32), p=0.000 |
| history_AFibYes | 1.60 (1.12-2.28), p=0.000 | 1.04 (0.72-1.49), p=0.791 | 1.04 (0.72-1.50), p=0.762 |
| med_CKDYes | 3.98 (2.48-6.41), p=0.000 | 2.66 (1.62-4.35), p=0.000 | 2.67 (1.63-4.37), p=0.000 |
| history_med_MIYes | 1.08 (0.86-1.35), p=0.342 | 1.03 (0.82-1.30), p=0.705 | 1.05 (0.83-1.32), p=0.560 |
| CMR_LVEF_5 | 0.82 (0.75-0.89), p=0.000 | 0.90 (0.83-0.99), p=0.001 | 0.92 (0.84-1.00), p=0.005 |
| CMR_LGE_ischemic_presencePresence_of_ischemic_LGE | 3.76 (2.78-5.08), p=0.000 | NA | 3.45 (2.55-4.67), p=0.000 |
# Export in word
ft <- flextable(table)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Model 3 - LGE presence in all pop (N=6,082)", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab5-multi_tradi-all-death-",Sys.Date(), ".docx")))Tab6-incr-all-death
var1 <- c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF")
var2 <- c(var1, "CMR_LGE_ischemic_presence")
df <- df_all %>% mutate(
time = outcome_FU_time_death,
event = outcome_death
)%>% droplevels()
# MODEL 1
results.model1 <- CoxPredictScore(derivation = df, validation = df,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var1,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model1$model_stats## NULLresults.model1$derivation$auc## [1] "0.7 [0.67-0.73]"# MODEL 2
results.model2 <- CoxPredictScore(derivation = df, validation = df,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var2,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model2$model_stats## NULLresults.model2$derivation$auc## [1] "0.75 [0.72-0.77]"roc.test(results.model1$roc_derivation$roc_obj, results.model2$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model1$roc_derivation$roc_obj and results.model2$roc_derivation$roc_obj
## Z = -5.614, p-value = 1.976e-08
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.06561281 -0.03165491
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.7021060 0.7507399Figures
Fig1-all-KM-LGE_presence
# Data parameters
results <- createSurvivalPlot(
data = df_all,
compared_with = "CMR_LGE_ischemic_presence",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#25A26B","#FF002B"),
confint_choosen = 0.995,
mytitle = "Fig1: LGE presence (all pop = 6,082)"
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_ischemic_presence"results$HR## HR_CI p_value
## 1 3.76 ( 2.78 - 5.08 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig1-all-KM-LGE_presence_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig1-all-KM-LGE_presence_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig2-all-KM-LGE_presence_multi
# Data parameters
df_selected <- df_all %>%
select(
outcome_FU_time_death, outcome_death,
CMR_LGE_ischemic_presence,
demo_age, demo_gender , demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF , history_AFib , med_CKD , history_med_MI , CMR_LVEF
) %>%
mutate(
outcome_FU_time_death = outcome_FU_time_death/12
) %>% filter(
outcome_FU_time_death <= 9
)
summary(df_selected$outcome_FU_time_death)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.08333 5.33333 6.58333 6.44798 7.83333 9.00000# statistics parameters
confint_choosen = 0.995
P_vizu = TRUE # To check log rank for all KM graphs
# Graphics viz
legend_title <- c("Ischemic LGE")
my_legends <- c("Absence of LGE","Presence of LGE")
mytitle <- c("Fig 1C - LGE MULTI - ALL (N=6082)")
my_colors <- c("#25A26B","#FF002B")
size_font = 12
ylim_choosen = c(0.5,1)
xlim_choosen = c(0,13)
#### ------------------------ ####
library(riskRegression)
model <- coxph(formula = Surv(outcome_FU_time_death, outcome_death) ~ CMR_LGE_ischemic_presence + demo_age+ demo_gender + demo_BMI + CV_risk_diabete + CV_risk_Smoking + CV_risk_dyslipidemia + history_hospit_HF + history_AFib + med_CKD + history_med_MI + CMR_LVEF, data = df_selected, x = TRUE)
adjsurv <- adjustedsurv(data=df_selected, # utilisation de ce filtre pour ne pas avoir l'ensemble de la courbe
variable="CMR_LGE_ischemic_presence",
ev_time="outcome_FU_time_death",
event="outcome_death",
method="direct",
outcome_model=model,
conf_int=TRUE,
conf_level = 0.995,
colors = TRUE,
custom_colors = my_colors,
ggtheme = theme_bw())
# plot with confidence intervals
gg <- plot(adjsurv,
main = mytitle,
legend.title = NULL,
legend.position = "none",
conf_int = TRUE,
censoring_ind = "lines",
xlab = "years of follow-up",
ylab = "Adjusted survival probability",
xlim = c(0, 12),
ylim = c(0.5, 1),
custom_colors = my_colors,
ggtheme = theme_bw()) +
scale_x_continuous(breaks = seq(0, 12, 2),
labels = seq(0, 12, 2)) +
theme(axis.text = element_text(size = size_font), # Adjust the font size of the axis labels
axis.title = element_text(size = size_font + 2), # Adjust the font size of the axis titles
axis.line = element_line(size = 0.1), # reduced size of axis
legend.text = element_text(size = size_font + 4), # Adjust the font size of the legend text
legend.title = element_text(size = size_font + 6), # Adjust the font size of the legend title
panel.border = element_rect(color = "black", fill = NA, size = 0.3),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
gg
ggsave(
filename = here(figures_output_dir, paste0("Fig2-all-KM-LGE_presence_multi-", Sys.Date(),".png")),
plot = gg, width = 10, height = 6, units = "in", dpi = 600)Fig3-all-KM-LGE_FEVG_plot
# Data parameters
df_selected <- df_all %>%
mutate(
FEVG_LGE = case_when(
CMR_LGE_ischemic_presence == "No_ischemic_LGE" & CMR_LVEF > 40 ~ "A LGE - ; FEVG ≥ 40",
CMR_LGE_ischemic_presence == "No_ischemic_LGE" & CMR_LVEF <= 40 ~ "B LGE - ; FEVG < 40",
CMR_LGE_ischemic_presence == "Presence_of_ischemic_LGE" & CMR_LVEF > 40 ~ "C LGE + ; FEVG ≥ 40",
CMR_LGE_ischemic_presence == "Presence_of_ischemic_LGE" & CMR_LVEF <= 40 ~ "D LGE + ; FEVG < 40",
TRUE ~ NA_character_
)
)
# Data parameters
results <- createSurvivalPlot(
data = df_selected,
compared_with = "FEVG_LGE",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#25A26B","#4292C6","#FF002B","#2C3E50"),
confint_choosen = 0.995,
mytitle = "Fig4: LGE type (all pop = 6,082)"
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ FEVG_LGE"results$HR## HR_CI p_value
## FEVG_LGEB LGE - ; FEVG < 40 1.12 ( 0.61 - 2.06 ) 0.596
## FEVG_LGEC LGE + ; FEVG ≥ 40 3.22 ( 2.24 - 4.63 ) <0.001
## FEVG_LGED LGE + ; FEVG < 40 5.61 ( 3.84 - 8.19 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig3-all-KM-LGE_FEVG_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig3-all-KM-LGE_FEVG_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig4-all-KM-LGE_type
# Data parameters
results <- createSurvivalPlot(
data = df_all,
compared_with = "CMR_LGE_type",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#25A26B","#4292C6","#FF002B","#2C3E50"),
confint_choosen = 0.995,
mytitle = "Fig4: LGE type (all pop = 6,082)"
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_type"results$HR## HR_CI p_value
## CMR_LGE_typeExclusive_midwall_LGE_presence 4.18 ( 2.38 - 7.33 ) <0.001
## CMR_LGE_typeExclusive_ischemic_LGE_presence 4.57 ( 3.11 - 6.72 ) <0.001
## CMR_LGE_typeischemic_and_midwall_LGE_presence 14.26 ( 9.22 - 22.06 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig4-all-KM-LGE_type_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig4-all-KM-LGE_type_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)KM Revascularization
Fig5-all-KM-revasc - in modif
results <- createSurvivalPlot(
data = df_all,
compared_with = "outcome_revascularisation_90days",
time = "outcome_FU_time_death",
event = "outcome_death",
confint_choosen = 0.995,
legend_title = "Fig revasc pop = 6,082"
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ outcome_revascularisation_90days"results$HR## HR_CI p_value
## 1 0.73 ( 0.58 - 0.91 ) <0.001results$ggsurv
ggsave(
filename = here(figures_output_dir, paste0("Fig5-all-KM-revasc_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig5-all-KM-revasc_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig6-all-KM-revasc_viability - in modif
# Modification of the dataset
df_selected <- df_all %>%
mutate(
CMR_LGE_Viable = case_when(
CMR_LGE_ischemic_transmurality == "B_Subendocardial<50%" ~ 1,
CMR_LGE_ischemic_transmurality == "C_Subendocardial≥50%" | CMR_LGE_ischemic_transmurality == "D_Transmural" ~ 2,
TRUE ~ 3),
CMR_LGE_Viable = factor(
CMR_LGE_Viable, levels = c(1,2,3), labels = c("Viable","NonViable","NoLGE")
),
CMR_LGE_Viable_Revasc = case_when(
CMR_LGE_Viable == "Viable" & outcome_revascularisation_90days == "Yes" ~ 1,
CMR_LGE_Viable == "Viable" & outcome_revascularisation_90days == "No" ~ 2,
CMR_LGE_Viable == "NonViable" & outcome_revascularisation_90days == "Yes" ~ 3,
CMR_LGE_Viable == "NonViable" & outcome_revascularisation_90days == "No" ~ 4,
TRUE ~ 0),
CMR_LGE_Viable_Revasc = factor(
CMR_LGE_Viable_Revasc, levels = c(0,1,2,3,4), labels = c("No_LGE","Viable_Revasc","viable_NonRevasc","NonViable_Revasc","NonViable_NonRevasc")
)
)
results <- createSurvivalPlot(
data = df_selected,
compared_with = "CMR_LGE_Viable_Revasc",
time = "outcome_FU_time_death",
event = "outcome_death",
confint_choosen = 0.995,
legend_title = "Fig revasc pop = 6,082",
my_colors = c("#006400", "darkblue", "#FF0000", "#8B4513", "#000000"),
show.CI = FALSE
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_Viable_Revasc"results$HR## HR_CI p_value
## CMR_LGE_Viable_RevascViable_Revasc 0.89 ( 0.56 - 1.42 ) 0.480
## CMR_LGE_Viable_Revascviable_NonRevasc 3.20 ( 1.69 - 6.05 ) <0.001
## CMR_LGE_Viable_RevascNonViable_Revasc 3.69 ( 2.62 - 5.20 ) <0.001
## CMR_LGE_Viable_RevascNonViable_NonRevasc 11.48 ( 8.30 - 15.88 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig6-all-KM-revasc_viability_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig6-all-KM-revasc_viability_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig7-all-corrplot
# Select specific columns for correlation analysis
df_selected <- df_LGE %>%
mutate_if(is.factor, ~ as.numeric(.)) %>%
select(demo_NIP:CMR_LGE_ischemic_presence) %>%
select(-outcome_death, -outcome_FU_time_death, -CMR_LGE_presence_ischemic_or_midwall, -demo_NIP, -demo_center)
# Calculate the correlation matrix and round the values
correlation_matrix <- cor(df_selected)
# Set the threshold for identifying highly correlated features
threshold <- 0.7
# Find highly correlated feature pairs
correlation_pairs <- which(correlation_matrix > threshold & correlation_matrix < 1, arr.ind = TRUE)
# Extract the names and correlation values of the most collinear features
most_collinear_features <- data.frame(
Feature1 = rownames(correlation_matrix)[correlation_pairs[, 1]],
Feature2 = colnames(correlation_matrix)[correlation_pairs[, 2]],
Correlation_Value = correlation_matrix[correlation_pairs]
)
# Sort by correlation value in descending order
most_collinear_features <- most_collinear_features[order(-most_collinear_features$Correlation_Value), ]
print(most_collinear_features)## Feature1 Feature2 Correlation_Value
## 7 CMR_LVESV CMR_LVEDV 0.9486931
## 8 CMR_LVEDV CMR_LVESV 0.9486931
## 1 history_AFib clini_cardiac_rythm 0.9486426
## 6 clini_cardiac_rythm history_AFib 0.9486426
## 2 demo_BMI demo_weight 0.8593656
## 3 demo_weight demo_BMI 0.8593656
## 4 CV_risk_obesity demo_BMI 0.7552200
## 5 demo_BMI CV_risk_obesity 0.7552200# Create a color palette for the plot
color_palette <- colorRampPalette(c("blue", "black", "red"))(50)
# Create an attractive correlation plot
correlation_matrix <- cor(df_selected[, unique(most_collinear_features$Feature1)])
corrplot(correlation_matrix,
method = "number", # Display correlation values
type = "upper", # Display only the upper triangle
col = color_palette, # Set the color palette
tl.cex = 0.8, # Adjust text label size
tl.col = "black", # Set text label color
cl.ratio = 0.2, # Adjust the space for the color legend
number.cex = 0.7, # Adjust correlation value size
addCoef.col = "black" # Set coefficient color
)
correlation_matrix <- cor(df_selected)
corrplot(correlation_matrix,
method = "color",
type = "full",
tl.col = "black",
addCoefasPercent = FALSE # Remove value display
)
corrplot(correlation_matrix,
method = "color",
type = "full",
tl.col = "black",
addCoefasPercent = FALSE) # Removes value display
# Saving the plot
# # Note: 'corr_plot' does not need to be assigned or used with 'plot = c' in ggsave
# ggsave(
# filename = here("outputs", "figures", paste0("Fig7-all-corrplot-", Sys.Date(), ".png")),
# plot = corr_plot, # Saves the last plot
# width = 10,
# height = 3,
# units = "in",
# dpi = 600
# )LGE population (N=3,591)
Survival
Tab7-univ-LGE-death
df_selected <- df_LGE %>%
mutate(
surv.event = Surv(
time = outcome_FU_time_death,
event = outcome_death
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
outcome_revascularisation_90days +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_presence_ischemic_and_midwall +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_midwall_extent_count +
CMR_LGE_midwall_extent_categ +
CMR_LGE_midwall_location_3 +
CMR_LGE_midwall_location_4 +
CMR_LGE_midwall_anterior +
CMR_LGE_midwall_septal +
CMR_LGE_midwall_inferior +
CMR_LGE_midwall_lateral +
CMR_LGE_midwall_apical,
data= df_selected,
method = 1, conf.level = 0.995),
hide.no = "No",
show.ratio=T, show.p.ratio = T)
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=3042 | N=549 | ||||
| demo_age | 64.1 (11.7) | 68.3 (11.3) | 1.03 [1.02;1.04] | <0.001 | <0.001 |
| demo_gender | 2194 (72.1%) | 485 (88.3%) | 2.64 [1.82;3.84] | <0.001 | <0.001 |
| demo_BMI | 27.7 (5.37) | 27.8 (5.65) | 1.00 [0.98;1.03] | 0.600 | 0.600 |
| CV_risk_diabete | 1137 (37.4%) | 309 (56.3%) | 1.96 [1.54;2.50] | <0.001 | <0.001 |
| CV_risk_HTA | 1627 (53.5%) | 277 (50.5%) | 0.90 [0.71;1.14] | 0.215 | 0.215 |
| CV_risk_obesity | 836 (27.5%) | 151 (27.5%) | 1.01 [0.77;1.33] | 0.892 | 0.888 |
| CV_risk_dyslipidemia | 1505 (49.5%) | 286 (52.1%) | 1.11 [0.87;1.41] | 0.224 | 0.222 |
| CV_risk_Smoking | 639 (21.0%) | 127 (23.1%) | 1.10 [0.83;1.47] | 0.328 | 0.329 |
| history_med_MI | 1167 (38.4%) | 220 (40.1%) | 1.07 [0.83;1.36] | 0.463 | 0.465 |
| history_coronary_procedure | 2679 (88.1%) | 488 (88.9%) | 1.06 [0.73;1.56] | 0.649 | 0.648 |
| history_interv_PCI | 198 (6.51%) | 59 (10.7%) | 1.57 [1.06;2.33] | 0.001 | 0.001 |
| history_interv_CABG | 2515 (82.7%) | 438 (79.8%) | 0.84 [0.62;1.13] | 0.100 | 0.098 |
| med_periph_atheroma | 156 (5.13%) | 58 (10.6%) | 1.99 [1.35;2.94] | <0.001 | <0.001 |
| history_stroke | 85 (2.79%) | 32 (5.83%) | 1.99 [1.20;3.33] | <0.001 | <0.001 |
| med_pacemaker | 21 (0.69%) | 3 (0.55%) | 0.83 [0.16;4.20] | 0.741 | 0.741 |
| med_CKD | 39 (1.28%) | 30 (5.46%) | 3.69 [2.18;6.26] | <0.001 | <0.001 |
| history_hospit_HF | 155 (5.10%) | 73 (13.3%) | 2.39 [1.68;3.41] | <0.001 | <0.001 |
| history_AFib | 207 (6.80%) | 61 (11.1%) | 1.61 [1.10;2.36] | <0.001 | <0.001 |
| clini_NYHA | 385 (12.7%) | 75 (13.7%) | 1.01 [0.71;1.44] | 0.919 | 0.918 |
| clini_cardiac_rythm | 197 (6.48%) | 52 (9.47%) | 1.42 [0.94;2.14] | 0.017 | 0.016 |
| outcome_revascularisation_90days | 2525 (83.0%) | 248 (45.2%) | 0.23 [0.18;0.29] | <0.001 | 0.000 |
| CMR_LVEF_5 | 8.78 (1.14) | 8.40 (1.27) | 0.82 [0.75;0.89] | <0.001 | <0.001 |
| CMR_LVEDV_5 | 10.2 (2.11) | 10.6 (2.38) | 1.05 [1.00;1.11] | 0.003 | 0.003 |
| CMR_LVESV_5 | 5.81 (1.74) | 6.20 (1.92) | 1.09 [1.03;1.16] | <0.001 | <0.001 |
| CMR_LV_mass | 91.1 (19.6) | 90.8 (19.5) | 1.00 [0.99;1.00] | 0.573 | 0.573 |
| CMR_RV_dysfunction | 89 (2.93%) | 58 (10.6%) | 3.15 [2.13;4.65] | <0.001 | 0.000 |
| CMR_LGE_presence_ischemic_and_midwall | 223 (7.33%) | 131 (23.9%) | 3.13 [2.36;4.15] | <0.001 | 0.000 |
| CMR_LGE_ischemic_presence: Presence_of_ischemic_LGE | 3042 (100%) | 549 (100%) | Ref. | Ref. | . |
| CMR_LGE_ischemic_extent_count | 2.74 (1.28) | 5.11 (1.72) | 1.90 [1.78;2.02] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| B_1_2_segments | 1369 (45.0%) | 23 (4.19%) | Ref. | Ref. | |
| C_3_5_segments | 1659 (54.5%) | 294 (53.6%) | 8.92 [4.86;16.4] | <0.001 | |
| D_more6_segments | 14 (0.46%) | 232 (42.3%) | 68.3 [37.0;126] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| B_Subendocardial<50% | 1619 (53.2%) | 79 (14.4%) | Ref. | Ref. | |
| C_Subendocardial≥50% | 1290 (42.4%) | 259 (47.2%) | 3.57 [2.49;5.12] | <0.001 | |
| D_Transmural | 133 (4.37%) | 211 (38.4%) | 14.7 [10.2;21.3] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| B_Neither_anterior_nor_septal | 2563 (84.3%) | 99 (18.0%) | Ref. | Ref. | |
| C_Anterior_without_septal | 317 (10.4%) | 105 (19.1%) | 7.22 [4.87;10.7] | <0.001 | |
| D_Septal | 162 (5.33%) | 345 (62.8%) | 21.9 [15.9;30.2] | <0.001 | |
| CMR_LGE_ischemic_anterior | 384 (12.6%) | 402 (73.2%) | 11.2 [8.56;14.7] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 162 (5.33%) | 345 (62.8%) | 12.2 [9.51;15.6] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 1097 (36.1%) | 117 (21.3%) | 0.53 [0.39;0.71] | <0.001 | <0.001 |
| CMR_LGE_ischemic_lateral | 966 (31.8%) | 229 (41.7%) | 1.41 [1.11;1.80] | <0.001 | <0.001 |
| CMR_LGE_ischemic_Apical | 875 (28.8%) | 293 (53.4%) | 2.44 [1.92;3.10] | <0.001 | 0.000 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 2819 (92.7%) | 418 (76.1%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 223 (7.33%) | 131 (23.9%) | 3.13 [2.36;4.15] | <0.001 | |
| CMR_LGE_midwall_extent_count | 0.08 (0.28) | 0.35 (0.68) | 2.44 [2.04;2.90] | <0.001 | <0.001 |
| CMR_LGE_midwall_extent_categ: | 0.000 | ||||
| None | 2819 (92.7%) | 418 (76.1%) | Ref. | Ref. | |
| Low_=1 | 213 (7.00%) | 76 (13.8%) | 2.22 [1.56;3.15] | <0.001 | |
| High_>1 | 10 (0.33%) | 55 (10.0%) | 7.47 [4.97;11.2] | <0.001 | |
| CMR_LGE_midwall_location_3: | 0.000 | ||||
| No_midwall_LGE | 2819 (92.7%) | 418 (76.1%) | Ref. | Ref. | |
| Midwall_LGE_not_at_risk | 179 (5.88%) | 52 (9.47%) | 1.73 [1.14;2.63] | <0.001 | |
| At_risk_midwall_LGE_(septal_and/or_lateral) | 44 (1.45%) | 79 (14.4%) | 6.41 [4.54;9.06] | <0.001 | |
| CMR_LGE_midwall_location_4: | 0.000 | ||||
| No_midwall_LGE | 2819 (92.7%) | 418 (76.1%) | Ref. | Ref. | |
| Midwall_LGE_not_at_risk | 179 (5.88%) | 52 (9.47%) | 1.73 [1.14;2.63] | <0.001 | |
| Lateral_midwall_LGE | 16 (0.53%) | 49 (8.93%) | 8.41 [5.50;12.9] | <0.001 | |
| Septal_Midwall_LGE | 28 (0.92%) | 30 (5.46%) | 4.63 [2.72;7.87] | <0.001 | |
| CMR_LGE_midwall_anterior | 82 (2.70%) | 17 (3.10%) | 1.02 [0.51;2.06] | 0.928 | 0.925 |
| CMR_LGE_midwall_septal | 28 (0.92%) | 30 (5.46%) | 4.03 [2.38;6.84] | <0.001 | <0.001 |
| CMR_LGE_midwall_inferior | 55 (1.81%) | 45 (8.20%) | 3.93 [2.54;6.08] | <0.001 | 0.000 |
| CMR_LGE_midwall_lateral | 16 (0.53%) | 54 (9.84%) | 7.70 [5.14;11.5] | <0.001 | 0.000 |
| CMR_LGE_midwall_apical | 43 (1.41%) | 12 (2.19%) | 1.29 [0.56;2.98] | 0.386 | 0.366 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab7-univ-LGE-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab8-multi_tradi-LGE-death
df <- df_LGE %>% mutate(
event = outcome_death,
time = outcome_FU_time_death,
CMR_LVEF_5 = CMR_LVEF/5
) %>% select(
event, time,
demo_age, demo_gender, demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF, history_AFib, med_CKD, history_med_MI , CMR_LVEF_5, CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_transmurality, CMR_LGE_ischemic_location_4, CMR_LGE_midwall_presence, outcome_revascularisation_90days) %>% droplevels()
#### TABLE
## Model 1
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LVEF_5))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 1", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.03 ( 0.02 - 0.04 ) | 1.03 ( 1.02 - 1.04 ) | <0.001 |
| demo_genderYes | 0.92 ( 0.54 - 1.30 ) | 2.51 ( 1.72 - 3.66 ) | <0.001 |
| demo_BMI | 0.00 ( -0.02 - 0.03 ) | 1.00 ( 0.98 - 1.03 ) | 0.680 |
| CV_risk_diabeteYes | 0.56 ( 0.30 - 0.81 ) | 1.74 ( 1.36 - 2.24 ) | <0.001 |
| CV_risk_SmokingYes | 0.26 ( -0.04 - 0.56 ) | 1.30 ( 0.96 - 1.76 ) | 0.014 |
| CV_risk_dyslipidemiaYes | 0.02 ( -0.23 - 0.26 ) | 1.02 ( 0.80 - 1.30 ) | 0.855 |
| history_hospit_HFYes | 0.54 ( 0.16 - 0.92 ) | 1.71 ( 1.17 - 2.50 ) | <0.001 |
| history_AFibYes | 0.07 ( -0.32 - 0.46 ) | 1.07 ( 0.72 - 1.59 ) | 0.621 |
| med_CKDYes | 0.92 ( 0.37 - 1.47 ) | 2.51 ( 1.45 - 4.33 ) | <0.001 |
| history_med_MIYes | 0.03 ( -0.22 - 0.28 ) | 1.03 ( 0.80 - 1.33 ) | 0.718 |
| CMR_LVEF_5 | -0.09 ( -0.19 - 0.00 ) | 0.91 ( 0.83 - 1.00 ) | 0.007 |
## Model 2
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 2", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( 0.01 - 0.03 ) | 1.02 ( 1.01 - 1.03 ) | <0.001 |
| demo_genderYes | 0.74 ( 0.36 - 1.12 ) | 2.09 ( 1.43 - 3.05 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.03 ) | 1.01 ( 0.98 - 1.03 ) | 0.417 |
| CV_risk_diabeteYes | 0.37 ( 0.12 - 0.62 ) | 1.45 ( 1.13 - 1.86 ) | <0.001 |
| CV_risk_SmokingYes | 0.25 ( -0.06 - 0.56 ) | 1.28 ( 0.94 - 1.74 ) | 0.024 |
| CV_risk_dyslipidemiaYes | 0.01 ( -0.24 - 0.25 ) | 1.01 ( 0.79 - 1.28 ) | 0.946 |
| history_hospit_HFYes | 0.15 ( -0.24 - 0.54 ) | 1.16 ( 0.79 - 1.72 ) | 0.276 |
| history_AFibYes | -0.03 ( -0.43 - 0.37 ) | 0.97 ( 0.65 - 1.44 ) | 0.819 |
| med_CKDYes | 0.84 ( 0.29 - 1.39 ) | 2.32 ( 1.34 - 4.00 ) | <0.001 |
| history_med_MIYes | -0.03 ( -0.29 - 0.22 ) | 0.97 ( 0.75 - 1.25 ) | 0.737 |
| CMR_LVEF_5 | -0.09 ( -0.18 - 0.01 ) | 0.92 ( 0.83 - 1.01 ) | 0.013 |
| CMR_LGE_ischemic_extent_count | 0.60 ( 0.53 - 0.66 ) | 1.82 ( 1.70 - 1.94 ) | <0.001 |
write.csv(data, file = here("outputs", "csv_files","model2_LGE_tradi.csv"), append = FALSE)
## Model 3A
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_transmurality))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3A", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.00 - 0.02 ) | 1.01 ( 1.00 - 1.02 ) | 0.007 |
| demo_genderYes | 0.67 ( 0.29 - 1.05 ) | 1.95 ( 1.33 - 2.85 ) | <0.001 |
| demo_BMI | -0.00 ( -0.03 - 0.03 ) | 1.00 ( 0.97 - 1.03 ) | 0.982 |
| CV_risk_diabeteYes | 0.34 ( 0.09 - 0.59 ) | 1.40 ( 1.09 - 1.80 ) | <0.001 |
| CV_risk_SmokingYes | 0.17 ( -0.14 - 0.48 ) | 1.19 ( 0.87 - 1.62 ) | 0.115 |
| CV_risk_dyslipidemiaYes | 0.04 ( -0.20 - 0.29 ) | 1.04 ( 0.82 - 1.33 ) | 0.644 |
| history_hospit_HFYes | 0.02 ( -0.39 - 0.42 ) | 1.02 ( 0.68 - 1.53 ) | 0.894 |
| history_AFibYes | -0.09 ( -0.49 - 0.32 ) | 0.92 ( 0.62 - 1.37 ) | 0.551 |
| med_CKDYes | 0.81 ( 0.26 - 1.36 ) | 2.25 ( 1.30 - 3.90 ) | <0.001 |
| history_med_MIYes | 0.05 ( -0.20 - 0.31 ) | 1.06 ( 0.82 - 1.37 ) | 0.550 |
| CMR_LVEF_5 | -0.06 ( -0.16 - 0.04 ) | 0.94 ( 0.85 - 1.04 ) | 0.084 |
| CMR_LGE_ischemic_extent_count | 0.50 ( 0.44 - 0.57 ) | 1.65 ( 1.55 - 1.77 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 1.04 ( 0.68 - 1.41 ) | 2.84 ( 1.97 - 4.09 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.67 ( 1.27 - 2.06 ) | 5.29 ( 3.55 - 7.88 ) | <0.001 |
## Model 3B
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_location_4))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3B", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( 0.00 - 0.03 ) | 1.01 ( 1.00 - 1.03 ) | 0.002 |
| demo_genderYes | 0.59 ( 0.21 - 0.98 ) | 1.81 ( 1.23 - 2.66 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.03 ) | 1.01 ( 0.98 - 1.03 ) | 0.569 |
| CV_risk_diabeteYes | 0.32 ( 0.07 - 0.57 ) | 1.38 ( 1.07 - 1.77 ) | <0.001 |
| CV_risk_SmokingYes | 0.14 ( -0.17 - 0.45 ) | 1.15 ( 0.85 - 1.57 ) | 0.201 |
| CV_risk_dyslipidemiaYes | 0.02 ( -0.23 - 0.26 ) | 1.02 ( 0.80 - 1.30 ) | 0.857 |
| history_hospit_HFYes | -0.00 ( -0.40 - 0.40 ) | 1.00 ( 0.67 - 1.49 ) | 0.994 |
| history_AFibYes | -0.28 ( -0.68 - 0.13 ) | 0.76 ( 0.51 - 1.13 ) | 0.053 |
| med_CKDYes | 0.69 ( 0.14 - 1.25 ) | 2.00 ( 1.15 - 3.48 ) | <0.001 |
| history_med_MIYes | -0.06 ( -0.32 - 0.19 ) | 0.94 ( 0.73 - 1.21 ) | 0.491 |
| CMR_LVEF_5 | -0.05 ( -0.14 - 0.05 ) | 0.96 ( 0.87 - 1.06 ) | 0.205 |
| CMR_LGE_ischemic_extent_count | 0.36 ( 0.29 - 0.43 ) | 1.44 ( 1.33 - 1.54 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.67 ( 1.27 - 2.07 ) | 5.33 ( 3.57 - 7.96 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 2.20 ( 1.83 - 2.56 ) | 9.00 ( 6.26 - 12.94 ) | <0.001 |
## Model 3C
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3C", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( 0.00 - 0.03 ) | 1.02 ( 1.00 - 1.03 ) | <0.001 |
| demo_genderYes | 0.77 ( 0.39 - 1.15 ) | 2.15 ( 1.47 - 3.14 ) | <0.001 |
| demo_BMI | 0.00 ( -0.02 - 0.03 ) | 1.00 ( 0.98 - 1.03 ) | 0.670 |
| CV_risk_diabeteYes | 0.38 ( 0.13 - 0.63 ) | 1.46 ( 1.14 - 1.88 ) | <0.001 |
| CV_risk_SmokingYes | 0.28 ( -0.03 - 0.59 ) | 1.32 ( 0.97 - 1.80 ) | 0.011 |
| CV_risk_dyslipidemiaYes | 0.04 ( -0.21 - 0.28 ) | 1.04 ( 0.81 - 1.33 ) | 0.661 |
| history_hospit_HFYes | 0.20 ( -0.19 - 0.59 ) | 1.22 ( 0.83 - 1.81 ) | 0.149 |
| history_AFibYes | -0.00 ( -0.40 - 0.40 ) | 1.00 ( 0.67 - 1.49 ) | 0.995 |
| med_CKDYes | 0.85 ( 0.31 - 1.40 ) | 2.35 ( 1.36 - 4.05 ) | <0.001 |
| history_med_MIYes | -0.04 ( -0.30 - 0.22 ) | 0.96 ( 0.74 - 1.24 ) | 0.663 |
| CMR_LVEF_5 | -0.07 ( -0.17 - 0.03 ) | 0.93 ( 0.84 - 1.03 ) | 0.048 |
| CMR_LGE_ischemic_extent_count | 0.57 ( 0.51 - 0.64 ) | 1.78 ( 1.66 - 1.89 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.68 ( 0.39 - 0.98 ) | 1.98 ( 1.48 - 2.66 ) | <0.001 |
## Model 4
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.00 - 0.02 ) | 1.01 ( 1.00 - 1.02 ) | 0.052 |
| demo_genderYes | 0.56 ( 0.17 - 0.94 ) | 1.75 ( 1.19 - 2.57 ) | <0.001 |
| demo_BMI | 0.00 ( -0.03 - 0.03 ) | 1.00 ( 0.97 - 1.03 ) | 0.961 |
| CV_risk_diabeteYes | 0.30 ( 0.05 - 0.55 ) | 1.35 ( 1.05 - 1.73 ) | <0.001 |
| CV_risk_SmokingYes | 0.12 ( -0.19 - 0.43 ) | 1.13 ( 0.83 - 1.54 ) | 0.283 |
| CV_risk_dyslipidemiaYes | 0.09 ( -0.16 - 0.34 ) | 1.10 ( 0.86 - 1.40 ) | 0.297 |
| history_hospit_HFYes | -0.01 ( -0.42 - 0.40 ) | 0.99 ( 0.66 - 1.48 ) | 0.942 |
| history_AFibYes | -0.24 ( -0.64 - 0.16 ) | 0.78 ( 0.53 - 1.17 ) | 0.090 |
| med_CKDYes | 0.71 ( 0.16 - 1.26 ) | 2.04 ( 1.17 - 3.54 ) | <0.001 |
| history_med_MIYes | -0.02 ( -0.28 - 0.24 ) | 0.98 ( 0.76 - 1.27 ) | 0.833 |
| CMR_LVEF_5 | -0.01 ( -0.11 - 0.09 ) | 0.99 ( 0.89 - 1.09 ) | 0.738 |
| CMR_LGE_ischemic_extent_count | 0.32 ( 0.24 - 0.39 ) | 1.37 ( 1.28 - 1.48 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 0.79 ( 0.42 - 1.16 ) | 2.21 ( 1.53 - 3.20 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.07 ( 0.67 - 1.48 ) | 2.92 ( 1.94 - 4.39 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.60 ( 1.20 - 2.01 ) | 4.96 ( 3.31 - 7.44 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 1.97 ( 1.59 - 2.34 ) | 7.14 ( 4.92 - 10.36 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.62 ( 0.33 - 0.91 ) | 1.86 ( 1.39 - 2.50 ) | <0.001 |
write.csv(data, file = here("outputs", "csv_files","model4_LGE_tradi.csv"), append = FALSE)
## Model 5
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: outcome_revascularisation_90days))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.01 - 0.02 ) | 1.01 ( 0.99 - 1.02 ) | 0.171 |
| demo_genderYes | 0.55 ( 0.17 - 0.94 ) | 1.74 ( 1.18 - 2.56 ) | <0.001 |
| demo_BMI | 0.00 ( -0.03 - 0.03 ) | 1.00 ( 0.97 - 1.03 ) | 0.927 |
| CV_risk_diabeteYes | 0.30 ( 0.05 - 0.55 ) | 1.35 ( 1.05 - 1.73 ) | <0.001 |
| CV_risk_SmokingYes | 0.11 ( -0.20 - 0.42 ) | 1.12 ( 0.82 - 1.53 ) | 0.316 |
| CV_risk_dyslipidemiaYes | 0.08 ( -0.17 - 0.33 ) | 1.09 ( 0.85 - 1.39 ) | 0.349 |
| history_hospit_HFYes | -0.02 ( -0.42 - 0.39 ) | 0.98 ( 0.66 - 1.48 ) | 0.910 |
| history_AFibYes | -0.23 ( -0.63 - 0.17 ) | 0.79 ( 0.53 - 1.19 ) | 0.107 |
| med_CKDYes | 0.71 ( 0.16 - 1.26 ) | 2.03 ( 1.17 - 3.52 ) | <0.001 |
| history_med_MIYes | -0.03 ( -0.29 - 0.23 ) | 0.97 ( 0.75 - 1.26 ) | 0.740 |
| CMR_LVEF_5 | -0.01 ( -0.11 - 0.09 ) | 0.99 ( 0.89 - 1.09 ) | 0.751 |
| CMR_LGE_ischemic_extent_count | 0.32 ( 0.24 - 0.39 ) | 1.37 ( 1.27 - 1.48 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 0.78 ( 0.42 - 1.15 ) | 2.19 ( 1.52 - 3.17 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 0.91 ( 0.43 - 1.38 ) | 2.47 ( 1.54 - 3.96 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.60 ( 1.19 - 2.00 ) | 4.94 ( 3.30 - 7.40 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 1.95 ( 1.58 - 2.33 ) | 7.05 ( 4.85 - 10.23 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.62 ( 0.32 - 0.91 ) | 1.85 ( 1.38 - 2.49 ) | <0.001 |
| outcome_revascularisation_90daysYes | -0.24 ( -0.59 - 0.11 ) | 0.79 ( 0.55 - 1.11 ) | 0.053 |
write.csv(data, file = here("outputs", "csv_files","model5_LGE_tradi.csv"), append = FALSE)
## MODELS MULTI NESTED
var_base = c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5")
### Nested 4
table <- uni_multi_function(
df = df,
event = "event",
time = "time",
var_base = var_base,
var_added = c("CMR_LGE_ischemic_extent_count", "CMR_LGE_ischemic_transmurality","CMR_LGE_ischemic_location_4",
"CMR_LGE_midwall_presence", "outcome_revascularisation_90days"))
# export in html
knitr::kable(table, caption = "Model full - granularity until revasc (LGE pop = 3,591)", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Variable | Univariate_Analysis | model_multi_1 | model_multi_2 | model_multi_3 | model_multi_4 | model_multi_5 | model_multi_6 |
|---|---|---|---|---|---|---|---|
| demo_age | 1.03 (1.02-1.04), p=0.000 | 1.03 (1.02-1.04), p=0.000 | 1.02 (1.01-1.03), p=0.000 | 1.01 (1.00-1.02), p=0.007 | 1.01 (1.00-1.02), p=0.017 | 1.01 (1.00-1.02), p=0.052 | 1.01 (0.99-1.02), p=0.171 |
| demo_genderYes | 2.64 (1.82-3.84), p=0.000 | 2.51 (1.72-3.66), p=0.000 | 2.09 (1.43-3.05), p=0.000 | 1.95 (1.33-2.85), p=0.000 | 1.75 (1.19-2.58), p=0.000 | 1.75 (1.19-2.57), p=0.000 | 1.74 (1.18-2.56), p=0.000 |
| demo_BMI | 1.00 (0.98-1.03), p=0.600 | 1.00 (0.98-1.03), p=0.680 | 1.01 (0.98-1.03), p=0.417 | 1.00 (0.97-1.03), p=0.982 | 1.00 (0.98-1.03), p=0.761 | 1.00 (0.97-1.03), p=0.961 | 1.00 (0.97-1.03), p=0.927 |
| CV_risk_diabeteYes | 1.96 (1.54-2.50), p=0.000 | 1.74 (1.36-2.24), p=0.000 | 1.45 (1.13-1.86), p=0.000 | 1.40 (1.09-1.80), p=0.000 | 1.36 (1.06-1.76), p=0.001 | 1.35 (1.05-1.73), p=0.001 | 1.35 (1.05-1.73), p=0.001 |
| CV_risk_SmokingYes | 1.10 (0.83-1.47), p=0.328 | 1.30 (0.96-1.76), p=0.014 | 1.28 (0.94-1.74), p=0.024 | 1.19 (0.87-1.62), p=0.115 | 1.10 (0.81-1.50), p=0.374 | 1.13 (0.83-1.54), p=0.283 | 1.12 (0.82-1.53), p=0.316 |
| CV_risk_dyslipidemiaYes | 1.11 (0.87-1.41), p=0.224 | 1.02 (0.80-1.30), p=0.855 | 1.01 (0.79-1.28), p=0.946 | 1.04 (0.82-1.33), p=0.644 | 1.03 (0.80-1.31), p=0.760 | 1.10 (0.86-1.40), p=0.297 | 1.09 (0.85-1.39), p=0.349 |
| history_hospit_HFYes | 2.39 (1.68-3.41), p=0.000 | 1.71 (1.17-2.50), p=0.000 | 1.16 (0.79-1.72), p=0.276 | 1.02 (0.68-1.53), p=0.894 | 0.92 (0.61-1.38), p=0.563 | 0.99 (0.66-1.48), p=0.942 | 0.98 (0.66-1.48), p=0.910 |
| history_AFibYes | 1.61 (1.10-2.36), p=0.000 | 1.07 (0.72-1.59), p=0.621 | 0.97 (0.65-1.44), p=0.819 | 0.92 (0.62-1.37), p=0.551 | 0.75 (0.50-1.12), p=0.047 | 0.78 (0.53-1.17), p=0.090 | 0.79 (0.53-1.19), p=0.107 |
| med_CKDYes | 3.69 (2.18-6.26), p=0.000 | 2.51 (1.45-4.33), p=0.000 | 2.32 (1.34-4.00), p=0.000 | 2.25 (1.30-3.90), p=0.000 | 1.97 (1.13-3.43), p=0.001 | 2.04 (1.17-3.54), p=0.000 | 2.03 (1.17-3.52), p=0.000 |
| history_med_MIYes | 1.07 (0.83-1.36), p=0.463 | 1.03 (0.80-1.33), p=0.718 | 0.97 (0.75-1.25), p=0.737 | 1.06 (0.82-1.37), p=0.550 | 1.01 (0.78-1.30), p=0.929 | 0.98 (0.76-1.27), p=0.833 | 0.97 (0.75-1.26), p=0.740 |
| CMR_LVEF_5 | 0.82 (0.75-0.89), p=0.000 | 0.91 (0.83-1.00), p=0.007 | 0.92 (0.83-1.01), p=0.013 | 0.94 (0.85-1.04), p=0.084 | 0.97 (0.87-1.07), p=0.357 | 0.99 (0.89-1.09), p=0.738 | 0.99 (0.89-1.09), p=0.751 |
| CMR_LGE_ischemic_extent_count | 1.90 (1.78-2.02), p=0.000 | NA | 1.82 (1.70-1.94), p=0.000 | 1.65 (1.55-1.77), p=0.000 | 1.38 (1.28-1.49), p=0.000 | 1.37 (1.28-1.48), p=0.000 | 1.37 (1.27-1.48), p=0.000 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 3.57 (2.49-5.12), p=0.000 | NA | NA | 2.84 (1.97-4.09), p=0.000 | 2.26 (1.56-3.27), p=0.000 | 2.21 (1.53-3.20), p=0.000 | 2.19 (1.52-3.17), p=0.000 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 14.73 (10.17-21.34), p=0.000 | NA | NA | 5.29 (3.55-7.88), p=0.000 | 3.14 (2.09-4.73), p=0.000 | 2.92 (1.94-4.39), p=0.000 | 2.47 (1.54-3.96), p=0.000 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 7.22 (4.87-10.70), p=0.000 | NA | NA | NA | 5.05 (3.38-7.55), p=0.000 | 4.96 (3.31-7.44), p=0.000 | 4.94 (3.30-7.40), p=0.000 |
| CMR_LGE_ischemic_location_4D_Septal | 21.91 (15.90-30.18), p=0.000 | NA | NA | NA | 7.09 (4.87-10.32), p=0.000 | 7.14 (4.92-10.36), p=0.000 | 7.05 (4.85-10.23), p=0.000 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 3.13 (2.36-4.15), p=0.000 | NA | NA | NA | NA | 1.86 (1.39-2.50), p=0.000 | 1.85 (1.38-2.49), p=0.000 |
| outcome_revascularisation_90daysYes | 0.23 (0.18-0.29), p=0.000 | NA | NA | NA | NA | NA | 0.79 (0.55-1.11), p=0.053 |
# Export in word
ft <- flextable(table)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Model 4 - LGE pop (N=3,591)", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab8-multi_tradi-LGE-death-",Sys.Date(), ".docx")))Tab9-multi_stepwise-LGE-death OK
library(dplyr)
library(StepReg)
library(survival)
# Assuming df_LGE is your initial dataset
df_selected <- df_all %>%
mutate_if(is.factor, as.numeric) %>% mutate(
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVESV_10 = CMR_LVESV/10,
CMR_LVEDV_10 = CMR_LVEDV/10
) %>%
select(outcome_death, outcome_FU_time_death,
demo_age, demo_gender, demo_BMI, CV_risk_diabete, CV_risk_obesity, CV_risk_dyslipidemia,
CV_risk_Smoking, history_med_MI, history_coronary_procedure, history_interv_PCI, history_interv_CABG, med_periph_atheroma, history_stroke, med_pacemaker, med_CKD, history_hospit_HF, history_AFib, clini_NYHA,
clini_cardiac_rythm, CMR_LVEF_5, CMR_LVESV_10, CMR_LVEDV_10,
CMR_LV_mass, CMR_RV_dysfunction) # CV_risk_HTA
# Define the Cox model formula
formula <- Surv(outcome_FU_time_death, outcome_death) ~ .
# Run stepwise Cox regression
stepwise_result <- stepwiseCox(
formula = formula,
data = df_selected,
sle = 0.1,
sls = 0.1,
selection = "forward", # Choose your selection method
select = "SL" #, # Choose your selection criterion
# method = "Breslow" # Choose your method for tie handling "Efron"
)
# View the results
summary(stepwise_result)## Length Class Mode
## Summary of Parameters 2 data.frame list
## Variables Type 2 data.frame list
## Process of Selection 6 data.frame list
## Selected Varaibles 11 data.frame list
## Coefficients of the Selected Variables 6 data.frame listvar_select <- as.data.frame(t(stepwise_result$`Selected Varaibles`[1,]))$xModel
var_select## [1] "demo_gender" "demo_age" "CV_risk_diabete"
## [4] "CMR_RV_dysfunction" "med_CKD" "history_hospit_HF"
## [7] "CMR_LVEF_5" "history_stroke" "med_periph_atheroma"
## [10] "med_pacemaker" "CV_risk_Smoking"#### Multivariable analysis
# taking the LGE variables into account
df <- df_LGE %>% mutate(
event = outcome_death,
time = outcome_FU_time_death,
CMR_LVEF_5 = CMR_LVEF/5
) %>% select(
event, time,
var_select, CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_transmurality, CMR_LGE_ischemic_location_4, CMR_LGE_midwall_presence, outcome_revascularisation_90days) %>% droplevels()
#### TABLE
## Model 1
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CV_risk_Smoking))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model stepwise 1", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_genderYes | 0.90 ( 0.52 - 1.28 ) | 2.45 ( 1.68 - 3.58 ) | <0.001 |
| demo_age | 0.03 ( 0.02 - 0.04 ) | 1.03 ( 1.02 - 1.04 ) | <0.001 |
| CV_risk_diabeteYes | 0.56 ( 0.32 - 0.81 ) | 1.76 ( 1.38 - 2.24 ) | <0.001 |
| CMR_RV_dysfunctionYes | 0.84 ( 0.44 - 1.25 ) | 2.33 ( 1.55 - 3.48 ) | <0.001 |
| med_CKDYes | 0.92 ( 0.38 - 1.47 ) | 2.52 ( 1.46 - 4.35 ) | <0.001 |
| history_hospit_HFYes | 0.47 ( 0.09 - 0.85 ) | 1.60 ( 1.09 - 2.34 ) | <0.001 |
| CMR_LVEF_5 | -0.07 ( -0.17 - 0.02 ) | 0.93 ( 0.85 - 1.02 ) | 0.029 |
| history_strokeYes | 0.55 ( 0.03 - 1.06 ) | 1.73 ( 1.03 - 2.89 ) | 0.003 |
| med_periph_atheromaYes | 0.34 ( -0.06 - 0.74 ) | 1.41 ( 0.94 - 2.09 ) | 0.016 |
| med_pacemakerYes | -0.86 ( -2.49 - 0.78 ) | 0.43 ( 0.08 - 2.19 ) | 0.142 |
| CV_risk_SmokingYes | 0.21 ( -0.09 - 0.51 ) | 1.23 ( 0.91 - 1.66 ) | 0.049 |
write.csv(data, file = here("outputs", "csv_files","model1_stepwise.csv"), append = FALSE)
## Model 2
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model stepwise 2", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_genderYes | 0.72 ( 0.34 - 1.10 ) | 2.05 ( 1.40 - 2.99 ) | <0.001 |
| demo_age | 0.02 ( 0.00 - 0.03 ) | 1.02 ( 1.00 - 1.03 ) | <0.001 |
| CV_risk_diabeteYes | 0.38 ( 0.13 - 0.62 ) | 1.46 ( 1.14 - 1.86 ) | <0.001 |
| CMR_RV_dysfunctionYes | 0.31 ( -0.10 - 0.72 ) | 1.36 ( 0.90 - 2.05 ) | 0.036 |
| med_CKDYes | 0.89 ( 0.36 - 1.43 ) | 2.45 ( 1.43 - 4.20 ) | <0.001 |
| history_hospit_HFYes | 0.11 ( -0.28 - 0.51 ) | 1.12 ( 0.75 - 1.66 ) | 0.427 |
| CMR_LVEF_5 | -0.08 ( -0.17 - 0.02 ) | 0.93 ( 0.84 - 1.02 ) | 0.022 |
| history_strokeYes | 0.61 ( 0.09 - 1.12 ) | 1.83 ( 1.09 - 3.07 ) | <0.001 |
| med_periph_atheromaYes | 0.22 ( -0.17 - 0.62 ) | 1.25 ( 0.84 - 1.86 ) | 0.115 |
| med_pacemakerYes | -0.78 ( -2.41 - 0.86 ) | 0.46 ( 0.09 - 2.36 ) | 0.182 |
| CV_risk_SmokingYes | 0.21 ( -0.09 - 0.52 ) | 1.24 ( 0.91 - 1.68 ) | 0.052 |
| CMR_LGE_ischemic_extent_count | 0.59 ( 0.52 - 0.65 ) | 1.80 ( 1.69 - 1.92 ) | <0.001 |
## Model 3A
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_transmurality))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model stepwise 3A", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_genderYes | 0.67 ( 0.29 - 1.05 ) | 1.95 ( 1.33 - 2.85 ) | <0.001 |
| demo_age | 0.01 ( -0.00 - 0.02 ) | 1.01 ( 1.00 - 1.02 ) | 0.007 |
| CV_risk_diabeteYes | 0.33 ( 0.09 - 0.58 ) | 1.40 ( 1.09 - 1.79 ) | <0.001 |
| CMR_RV_dysfunctionYes | 0.11 ( -0.31 - 0.52 ) | 1.11 ( 0.73 - 1.69 ) | 0.470 |
| med_CKDYes | 0.81 ( 0.26 - 1.35 ) | 2.24 ( 1.30 - 3.86 ) | <0.001 |
| history_hospit_HFYes | -0.01 ( -0.42 - 0.39 ) | 0.99 ( 0.66 - 1.48 ) | 0.929 |
| CMR_LVEF_5 | -0.07 ( -0.16 - 0.03 ) | 0.94 ( 0.85 - 1.03 ) | 0.057 |
| history_strokeYes | 0.55 ( 0.04 - 1.07 ) | 1.74 ( 1.04 - 2.90 ) | 0.003 |
| med_periph_atheromaYes | 0.10 ( -0.30 - 0.50 ) | 1.11 ( 0.74 - 1.65 ) | 0.478 |
| med_pacemakerYes | -0.81 ( -2.44 - 0.83 ) | 0.45 ( 0.09 - 2.30 ) | 0.167 |
| CV_risk_SmokingYes | 0.16 ( -0.14 - 0.47 ) | 1.18 ( 0.87 - 1.60 ) | 0.131 |
| CMR_LGE_ischemic_extent_count | 0.50 ( 0.43 - 0.57 ) | 1.65 ( 1.54 - 1.77 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 1.02 ( 0.66 - 1.39 ) | 2.79 ( 1.94 - 4.01 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.64 ( 1.24 - 2.04 ) | 5.15 ( 3.45 - 7.68 ) | <0.001 |
## Model 3B
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_location_4))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model stepwise 3B", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_genderYes | 0.59 ( 0.20 - 0.97 ) | 1.80 ( 1.23 - 2.65 ) | <0.001 |
| demo_age | 0.01 ( -0.00 - 0.02 ) | 1.01 ( 1.00 - 1.02 ) | 0.007 |
| CV_risk_diabeteYes | 0.31 ( 0.07 - 0.56 ) | 1.37 ( 1.07 - 1.75 ) | <0.001 |
| CMR_RV_dysfunctionYes | 0.11 ( -0.30 - 0.52 ) | 1.12 ( 0.74 - 1.68 ) | 0.438 |
| med_CKDYes | 0.67 ( 0.13 - 1.22 ) | 1.96 ( 1.14 - 3.38 ) | <0.001 |
| history_hospit_HFYes | 0.00 ( -0.39 - 0.40 ) | 1.00 ( 0.68 - 1.49 ) | 0.991 |
| CMR_LVEF_5 | -0.03 ( -0.13 - 0.06 ) | 0.97 ( 0.88 - 1.07 ) | 0.334 |
| history_strokeYes | 0.68 ( 0.16 - 1.20 ) | 1.97 ( 1.17 - 3.31 ) | <0.001 |
| med_periph_atheromaYes | 0.06 ( -0.33 - 0.46 ) | 1.07 ( 0.72 - 1.58 ) | 0.656 |
| med_pacemakerYes | 0.35 ( -1.30 - 2.00 ) | 1.42 ( 0.27 - 7.40 ) | 0.549 |
| CV_risk_SmokingYes | 0.13 ( -0.18 - 0.43 ) | 1.14 ( 0.84 - 1.54 ) | 0.245 |
| CMR_LGE_ischemic_extent_count | 0.36 ( 0.28 - 0.43 ) | 1.43 ( 1.33 - 1.54 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.65 ( 1.25 - 2.06 ) | 5.23 ( 3.50 - 7.82 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 2.19 ( 1.83 - 2.56 ) | 8.97 ( 6.22 - 12.93 ) | <0.001 |
## Model 3C
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model stepwise 3C", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_genderYes | 0.75 ( 0.37 - 1.13 ) | 2.13 ( 1.45 - 3.11 ) | <0.001 |
| demo_age | 0.02 ( 0.00 - 0.03 ) | 1.02 ( 1.00 - 1.03 ) | <0.001 |
| CV_risk_diabeteYes | 0.38 ( 0.14 - 0.63 ) | 1.47 ( 1.15 - 1.88 ) | <0.001 |
| CMR_RV_dysfunctionYes | 0.26 ( -0.16 - 0.67 ) | 1.29 ( 0.85 - 1.96 ) | 0.081 |
| med_CKDYes | 0.90 ( 0.37 - 1.44 ) | 2.47 ( 1.44 - 4.23 ) | <0.001 |
| history_hospit_HFYes | 0.17 ( -0.23 - 0.56 ) | 1.18 ( 0.79 - 1.76 ) | 0.238 |
| CMR_LVEF_5 | -0.06 ( -0.16 - 0.03 ) | 0.94 ( 0.85 - 1.04 ) | 0.073 |
| history_strokeYes | 0.62 ( 0.11 - 1.14 ) | 1.86 ( 1.11 - 3.12 ) | <0.001 |
| med_periph_atheromaYes | 0.20 ( -0.20 - 0.60 ) | 1.22 ( 0.82 - 1.82 ) | 0.162 |
| med_pacemakerYes | -0.81 ( -2.44 - 0.83 ) | 0.45 ( 0.09 - 2.30 ) | 0.167 |
| CV_risk_SmokingYes | 0.25 ( -0.06 - 0.55 ) | 1.28 ( 0.94 - 1.74 ) | 0.023 |
| CMR_LGE_ischemic_extent_count | 0.57 ( 0.50 - 0.63 ) | 1.76 ( 1.65 - 1.88 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.68 ( 0.39 - 0.97 ) | 1.97 ( 1.47 - 2.64 ) | <0.001 |
## Model 4
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model stepwise 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_genderYes | 0.57 ( 0.18 - 0.96 ) | 1.77 ( 1.20 - 2.60 ) | <0.001 |
| demo_age | 0.01 ( -0.00 - 0.02 ) | 1.01 ( 1.00 - 1.02 ) | 0.088 |
| CV_risk_diabeteYes | 0.29 ( 0.05 - 0.54 ) | 1.34 ( 1.05 - 1.71 ) | <0.001 |
| CMR_RV_dysfunctionYes | -0.06 ( -0.47 - 0.35 ) | 0.94 ( 0.62 - 1.42 ) | 0.683 |
| med_CKDYes | 0.69 ( 0.14 - 1.23 ) | 1.98 ( 1.15 - 3.41 ) | <0.001 |
| history_hospit_HFYes | -0.00 ( -0.41 - 0.40 ) | 1.00 ( 0.67 - 1.49 ) | 0.978 |
| CMR_LVEF_5 | -0.01 ( -0.11 - 0.09 ) | 0.99 ( 0.90 - 1.09 ) | 0.781 |
| history_strokeYes | 0.69 ( 0.17 - 1.21 ) | 2.00 ( 1.19 - 3.36 ) | <0.001 |
| med_periph_atheromaYes | -0.02 ( -0.42 - 0.38 ) | 0.98 ( 0.66 - 1.46 ) | 0.897 |
| med_pacemakerYes | 0.33 ( -1.32 - 1.97 ) | 1.38 ( 0.27 - 7.18 ) | 0.579 |
| CV_risk_SmokingYes | 0.12 ( -0.18 - 0.43 ) | 1.13 ( 0.83 - 1.54 ) | 0.260 |
| CMR_LGE_ischemic_extent_count | 0.31 ( 0.24 - 0.39 ) | 1.37 ( 1.27 - 1.48 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 0.77 ( 0.40 - 1.14 ) | 2.16 ( 1.49 - 3.13 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.07 ( 0.66 - 1.47 ) | 2.90 ( 1.93 - 4.36 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.59 ( 1.19 - 2.00 ) | 4.92 ( 3.28 - 7.37 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 1.98 ( 1.60 - 2.35 ) | 7.21 ( 4.96 - 10.49 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.64 ( 0.35 - 0.93 ) | 1.89 ( 1.41 - 2.53 ) | <0.001 |
write.csv(data, file = here("outputs", "csv_files","model4_stepwise.csv"), append = FALSE)
## model uni - multi
table <- uni_multi_function(
df = df,
event = "event",
time = "time",
var_base = var_select,
var_added = c("CMR_LGE_ischemic_extent_count", "CMR_LGE_ischemic_transmurality","CMR_LGE_ischemic_location_4",
"CMR_LGE_midwall_presence", "outcome_revascularisation_90days"))
# export in html
knitr::kable(table, caption = "Model 4 - granularity + revasc (LGE pop = 3,591)", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Variable | Univariate_Analysis | model_multi_1 | model_multi_2 | model_multi_3 | model_multi_4 | model_multi_5 | model_multi_6 |
|---|---|---|---|---|---|---|---|
| demo_genderYes | 2.64 (1.82-3.84), p=0.000 | 2.45 (1.68-3.58), p=0.000 | 2.05 (1.40-2.99), p=0.000 | 1.95 (1.33-2.85), p=0.000 | 1.75 (1.19-2.58), p=0.000 | 1.77 (1.20-2.60), p=0.000 | 1.76 (1.20-2.59), p=0.000 |
| demo_age | 1.03 (1.02-1.04), p=0.000 | 1.03 (1.02-1.04), p=0.000 | 1.02 (1.00-1.03), p=0.000 | 1.01 (1.00-1.02), p=0.007 | 1.01 (1.00-1.02), p=0.053 | 1.01 (1.00-1.02), p=0.088 | 1.00 (0.99-1.02), p=0.262 |
| CV_risk_diabeteYes | 1.96 (1.54-2.50), p=0.000 | 1.76 (1.38-2.24), p=0.000 | 1.46 (1.14-1.86), p=0.000 | 1.40 (1.09-1.79), p=0.000 | 1.35 (1.06-1.73), p=0.001 | 1.34 (1.05-1.71), p=0.001 | 1.34 (1.05-1.71), p=0.001 |
| CMR_RV_dysfunctionYes | 3.15 (2.13-4.65), p=0.000 | 2.33 (1.55-3.48), p=0.000 | 1.36 (0.90-2.05), p=0.036 | 1.11 (0.73-1.69), p=0.470 | 1.01 (0.67-1.52), p=0.948 | 0.94 (0.62-1.42), p=0.683 | 0.94 (0.62-1.42), p=0.685 |
| med_CKDYes | 3.69 (2.18-6.26), p=0.000 | 2.52 (1.46-4.35), p=0.000 | 2.45 (1.43-4.20), p=0.000 | 2.24 (1.30-3.86), p=0.000 | 1.93 (1.12-3.32), p=0.001 | 1.98 (1.15-3.41), p=0.000 | 1.99 (1.16-3.42), p=0.000 |
| history_hospit_HFYes | 2.39 (1.68-3.41), p=0.000 | 1.60 (1.09-2.34), p=0.001 | 1.12 (0.75-1.66), p=0.427 | 0.99 (0.66-1.48), p=0.929 | 0.93 (0.62-1.39), p=0.603 | 1.00 (0.67-1.49), p=0.978 | 0.99 (0.66-1.48), p=0.953 |
| CMR_LVEF_5 | 0.82 (0.75-0.89), p=0.000 | 0.93 (0.85-1.02), p=0.029 | 0.93 (0.84-1.02), p=0.022 | 0.94 (0.85-1.03), p=0.057 | 0.97 (0.88-1.07), p=0.385 | 0.99 (0.90-1.09), p=0.781 | 0.99 (0.90-1.10), p=0.821 |
| history_strokeYes | 1.99 (1.20-3.33), p=0.000 | 1.73 (1.03-2.89), p=0.003 | 1.83 (1.09-3.07), p=0.001 | 1.74 (1.04-2.90), p=0.003 | 1.88 (1.12-3.16), p=0.001 | 2.00 (1.19-3.36), p=0.000 | 1.97 (1.17-3.32), p=0.000 |
| med_periph_atheromaYes | 1.99 (1.35-2.94), p=0.000 | 1.41 (0.94-2.09), p=0.016 | 1.25 (0.84-1.86), p=0.115 | 1.11 (0.74-1.65), p=0.478 | 1.01 (0.68-1.51), p=0.940 | 0.98 (0.66-1.46), p=0.897 | 0.98 (0.65-1.46), p=0.864 |
| med_pacemakerYes | 0.83 (0.16-4.20), p=0.741 | 0.43 (0.08-2.19), p=0.142 | 0.46 (0.09-2.36), p=0.182 | 0.45 (0.09-2.30), p=0.167 | 1.34 (0.26-6.93), p=0.622 | 1.38 (0.27-7.18), p=0.579 | 1.34 (0.26-6.97), p=0.615 |
| CV_risk_SmokingYes | 1.10 (0.83-1.47), p=0.328 | 1.23 (0.91-1.66), p=0.049 | 1.24 (0.91-1.68), p=0.052 | 1.18 (0.87-1.60), p=0.131 | 1.10 (0.81-1.49), p=0.391 | 1.13 (0.83-1.54), p=0.260 | 1.12 (0.82-1.53), p=0.291 |
| CMR_LGE_ischemic_extent_count | 1.90 (1.78-2.02), p=0.000 | NA | 1.80 (1.69-1.92), p=0.000 | 1.65 (1.54-1.77), p=0.000 | 1.38 (1.28-1.48), p=0.000 | 1.37 (1.27-1.48), p=0.000 | 1.37 (1.27-1.47), p=0.000 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 3.57 (2.49-5.12), p=0.000 | NA | NA | 2.79 (1.94-4.01), p=0.000 | 2.22 (1.53-3.21), p=0.000 | 2.16 (1.49-3.13), p=0.000 | 2.15 (1.48-3.11), p=0.000 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 14.73 (10.17-21.34), p=0.000 | NA | NA | 5.15 (3.45-7.68), p=0.000 | 3.10 (2.06-4.66), p=0.000 | 2.90 (1.93-4.36), p=0.000 | 2.47 (1.55-3.94), p=0.000 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 7.22 (4.87-10.70), p=0.000 | NA | NA | NA | 4.96 (3.31-7.43), p=0.000 | 4.92 (3.28-7.37), p=0.000 | 4.90 (3.27-7.34), p=0.000 |
| CMR_LGE_ischemic_location_4D_Septal | 21.91 (15.90-30.18), p=0.000 | NA | NA | NA | 7.13 (4.89-10.40), p=0.000 | 7.21 (4.96-10.49), p=0.000 | 7.12 (4.89-10.35), p=0.000 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 3.13 (2.36-4.15), p=0.000 | NA | NA | NA | NA | 1.89 (1.41-2.53), p=0.000 | 1.89 (1.41-2.53), p=0.000 |
| outcome_revascularisation_90daysYes | 0.23 (0.18-0.29), p=0.000 | NA | NA | NA | NA | NA | 0.79 (0.55-1.11), p=0.053 |
# Export in word
ft <- flextable(table)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Model full - LGE pop (N=3,591)", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab9-multi_stepwise-LGE-death-",Sys.Date(), ".docx")))Tab10-incr_tradi-LGE-death
var1 <- c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5")
var2 <- c(var1, "CMR_LGE_ischemic_extent_count")
var3A <- c(var2, "CMR_LGE_ischemic_transmurality")
var3B <- c(var2, "CMR_LGE_ischemic_location_4")
var3C <- c(var2, "CMR_LGE_midwall_presence")
var4 <- c(var2, "CMR_LGE_ischemic_transmurality", "CMR_LGE_ischemic_location_4", "CMR_LGE_midwall_presence")
df_LGE <- df_LGE %>% mutate(
time = outcome_FU_time_death,
event = outcome_death,
CMR_LVEF_5 = CMR_LVEF/5
)%>% droplevels()
# MODEL 1
results.model1 <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var1,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model1$model_stats## NULLresults.model1$derivation$auc## [1] "0.69 [0.65-0.72]"# MODEL 2
results.model2 <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var2,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model2$model_stats## NULLresults.model2$derivation$auc## [1] "0.84 [0.81-0.86]"roc.test(results.model1$roc_derivation$roc_obj, results.model2$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model1$roc_derivation$roc_obj and results.model2$roc_derivation$roc_obj
## Z = -10.482, p-value < 2.2e-16
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.1788057 -0.1224720
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.6854679 0.8361068# MODEL 3A
results.model3A <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3A,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3A$model_stats## NULLresults.model3A$derivation$auc## [1] "0.87 [0.84-0.89]"roc.test(results.model2$roc_derivation$roc_obj, results.model3A$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3A$roc_derivation$roc_obj
## Z = -4.5914, p-value = 4.402e-06
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.04520743 -0.01815825
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8361068 0.8677896# MODEL 3B
results.model3B <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3B,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3B$model_stats## NULLresults.model3B$derivation$auc## [1] "0.89 [0.87-0.91]"roc.test(results.model2$roc_derivation$roc_obj, results.model3B$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3B$roc_derivation$roc_obj
## Z = -5.9401, p-value = 2.849e-09
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.06732729 -0.03392017
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8361068 0.8867305# MODEL 3C
results.model3C <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3C,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3C$model_stats## NULLresults.model3C$derivation$auc## [1] "0.85 [0.82-0.87]"roc.test(results.model2$roc_derivation$roc_obj, results.model3C$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3C$roc_derivation$roc_obj
## Z = -2.8387, p-value = 0.00453
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.014070016 -0.002576424
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8361068 0.8444300# MODEL 4
results.model4 <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var4,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model4$model_stats## NULLresults.model4$derivation$auc## [1] "0.9 [0.88-0.92]"roc.test(results.model2$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -6.8675, p-value = 6.532e-12
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.08176242 -0.04545513
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8361068 0.8997156# incremental between model 4 and model 3ABC
roc.test(results.model3A$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model3A$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -4.7942, p-value = 1.633e-06
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.04497781 -0.01887406
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8677896 0.8997156roc.test(results.model3B$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model3B$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -3.6407, p-value = 0.0002719
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.019975469 -0.005994622
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8867305 0.8997156roc.test(results.model3C$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model3C$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -6.3427, p-value = 2.257e-10
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.07236935 -0.03820176
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8444300 0.8997156Tab11-incr_stepwise-LGE-death
# Assuming df_LGE is your initial dataset
df_selected <- df_all %>%
mutate_if(is.factor, as.numeric) %>% mutate(
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVESV_10 = CMR_LVESV/10,
CMR_LVEDV_10 = CMR_LVEDV/10
) %>%
select(outcome_death, outcome_FU_time_death,
demo_age, demo_gender, demo_BMI, CV_risk_diabete, CV_risk_obesity, CV_risk_dyslipidemia,
CV_risk_Smoking, history_med_MI, history_coronary_procedure, history_interv_PCI, history_interv_CABG, med_periph_atheroma, history_stroke, med_pacemaker, med_CKD, history_hospit_HF, history_AFib, clini_NYHA,
clini_cardiac_rythm, CMR_LVEF_5, CMR_LVESV_10, CMR_LVEDV_10,
CMR_LV_mass, CMR_RV_dysfunction) # CV_risk_HTA
# Define the Cox model formula
formula <- Surv(outcome_FU_time_death, outcome_death) ~ .
# Run stepwise Cox regression
stepwise_result <- stepwiseCox(
formula = formula,
data = df_selected,
sle = 0.1,
sls = 0.1,
selection = "forward", # Choose your selection method
select = "SL" #, # Choose your selection criterion
# method = "Breslow" # Choose your method for tie handling "Efron"
)
# View the results
summary(stepwise_result)## Length Class Mode
## Summary of Parameters 2 data.frame list
## Variables Type 2 data.frame list
## Process of Selection 6 data.frame list
## Selected Varaibles 11 data.frame list
## Coefficients of the Selected Variables 6 data.frame listvar_select <- as.data.frame(t(stepwise_result$`Selected Varaibles`[1,]))$xModel
var_select## [1] "demo_gender" "demo_age" "CV_risk_diabete"
## [4] "CMR_RV_dysfunction" "med_CKD" "history_hospit_HF"
## [7] "CMR_LVEF_5" "history_stroke" "med_periph_atheroma"
## [10] "med_pacemaker" "CV_risk_Smoking"# View the results
summary(stepwise_result)## Length Class Mode
## Summary of Parameters 2 data.frame list
## Variables Type 2 data.frame list
## Process of Selection 6 data.frame list
## Selected Varaibles 11 data.frame list
## Coefficients of the Selected Variables 6 data.frame listvar1 <- as.data.frame(t(stepwise_result$`Selected Varaibles`[1,]))$xModel # on prend les résultats du stepwise
var2 <- c(var1, "CMR_LGE_ischemic_extent_count")
var3A <- c(var2, "CMR_LGE_ischemic_transmurality")
var3B <- c(var2, "CMR_LGE_ischemic_location_4")
var3C <- c(var2, "CMR_LGE_midwall_presence")
var4 <- c(var2, "CMR_LGE_ischemic_transmurality", "CMR_LGE_ischemic_location_4", "CMR_LGE_midwall_presence")
df_LGE <- df_LGE %>% mutate(
time = outcome_FU_time_death,
event = outcome_death,
CMR_LVEF_5 = CMR_LVEF/5,
)%>% droplevels()
# MODEL 1
results.model1 <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var1,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model1$model_stats## NULLresults.model1$derivation$auc## [1] "0.7 [0.67-0.73]"# MODEL 2
results.model2 <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var2,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model2$model_stats## NULLresults.model2$derivation$auc## [1] "0.84 [0.81-0.87]"roc.test(results.model1$roc_derivation$roc_obj, results.model2$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model1$roc_derivation$roc_obj and results.model2$roc_derivation$roc_obj
## Z = -10.226, p-value < 2.2e-16
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.1659197 -0.1125463
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.6999564 0.8391894# MODEL 3A
results.model3A <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3A,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3A$model_stats## NULLresults.model3A$derivation$auc## [1] "0.87 [0.84-0.89]"roc.test(results.model2$roc_derivation$roc_obj, results.model3A$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3A$roc_derivation$roc_obj
## Z = -4.3781, p-value = 1.197e-05
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.04311206 -0.01644824
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8391894 0.8689696# MODEL 3B
results.model3B <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3B,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3B$model_stats## NULLresults.model3B$derivation$auc## [1] "0.89 [0.87-0.91]"roc.test(results.model2$roc_derivation$roc_obj, results.model3B$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3B$roc_derivation$roc_obj
## Z = -5.7315, p-value = 9.956e-09
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.06458015 -0.03166701
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8391894 0.8873130# MODEL 3C
results.model3C <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3C,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3C$model_stats## NULLresults.model3C$derivation$auc## [1] "0.85 [0.82-0.88]"roc.test(results.model2$roc_derivation$roc_obj, results.model3C$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3C$roc_derivation$roc_obj
## Z = -2.9845, p-value = 0.00284
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.014321962 -0.002967746
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8391894 0.8478343# MODEL 4
results.model4 <- CoxPredictScore(derivation = df_LGE, validation = df_LGE,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var4,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model4$model_stats## NULLresults.model4$derivation$auc## [1] "0.9 [0.88-0.92]"roc.test(results.model2$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -6.6266, p-value = 3.435e-11
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.07912464 -0.04300285
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8391894 0.9002532Figures
Fig8-Schoenfelds_residuals
df_selected <- df_LGE %>%
mutate(
CMR_LVEF_5 = CMR_LVEF/5
) %>%
select(
outcome_FU_time_death, outcome_death,
CMR_LGE_ischemic_extent_count,
CMR_LGE_ischemic_transmurality,
CMR_LGE_ischemic_location_4,
CMR_LGE_midwall_presence,
demo_age, demo_gender , demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF , history_AFib , med_CKD , history_med_MI , CMR_LVEF_5
) %>% droplevels()
model_3 <- coxph(formula = Surv(outcome_FU_time_death, outcome_death) ~ .,
data = df_selected)
cox.zph(model_3)## chisq df p
## CMR_LGE_ischemic_extent_count 9.54e-01 1 0.329
## CMR_LGE_ischemic_transmurality 5.60e+00 2 0.061
## CMR_LGE_ischemic_location_4 1.34e+00 2 0.512
## CMR_LGE_midwall_presence 3.97e-01 1 0.529
## demo_age 1.63e+00 1 0.202
## demo_gender 1.67e-01 1 0.683
## demo_BMI 1.89e-02 1 0.891
## CV_risk_diabete 6.91e-03 1 0.934
## CV_risk_Smoking 1.54e-01 1 0.695
## CV_risk_dyslipidemia 1.26e-05 1 0.997
## history_hospit_HF 1.57e-02 1 0.900
## history_AFib 5.82e-01 1 0.446
## med_CKD 2.42e-02 1 0.876
## history_med_MI 8.76e-01 1 0.349
## CMR_LVEF_5 4.70e+00 1 0.030
## GLOBAL 2.05e+01 17 0.247tbl_regression(model_3, exponentiate = TRUE, conf.level = 0.995, pvalue_fun = function(x) style_pvalue(x, digits = 3), label = list()) %>% bold_p() %>%
as_gt() %>% gt::tab_header(gt::md("Modèle 3 - risk litterature + LGE extent + granularity without multi"))| Modèle 3 - risk litterature + LGE extent + granularity without multi | |||
| Characteristic | HR1 | 100% CI1 | p-value |
|---|---|---|---|
| CMR_LGE_ischemic_extent_count | 1.37 | 1.28, 1.48 | <0.001 |
| CMR_LGE_ischemic_transmurality | |||
|     B_Subendocardial<50% | — | — | |
|     C_Subendocardial≥50% | 2.21 | 1.53, 3.20 | <0.001 |
| Â Â Â Â D_Transmural | 2.92 | 1.94, 4.39 | <0.001 |
| CMR_LGE_ischemic_location_4 | |||
|     B_Neither_anterior_nor_septal | — | — | |
| Â Â Â Â C_Anterior_without_septal | 4.96 | 3.31, 7.44 | <0.001 |
| Â Â Â Â D_Septal | 7.14 | 4.92, 10.4 | <0.001 |
| CMR_LGE_midwall_presence | |||
|     A_No_midwall_LGE | — | — | |
| Â Â Â Â B_Presence_of_midwall_LGE | 1.86 | 1.39, 2.50 | <0.001 |
| demo_age | 1.01 | 1.00, 1.02 | 0.052 |
| demo_gender | |||
|     No | — | — | |
| Â Â Â Â Yes | 1.75 | 1.19, 2.57 | <0.001 |
| demo_BMI | 1.00 | 0.97, 1.03 | 0.961 |
| CV_risk_diabete | |||
|     No | — | — | |
| Â Â Â Â Yes | 1.35 | 1.05, 1.73 | <0.001 |
| CV_risk_Smoking | |||
|     No | — | — | |
| Â Â Â Â Yes | 1.13 | 0.83, 1.54 | 0.283 |
| CV_risk_dyslipidemia | |||
|     No | — | — | |
| Â Â Â Â Yes | 1.10 | 0.86, 1.40 | 0.297 |
| history_hospit_HF | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.99 | 0.66, 1.48 | 0.942 |
| history_AFib | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.78 | 0.53, 1.17 | 0.090 |
| med_CKD | |||
|     No | — | — | |
| Â Â Â Â Yes | 2.04 | 1.17, 3.54 | <0.001 |
| history_med_MI | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.98 | 0.76, 1.27 | 0.833 |
| CMR_LVEF_5 | 0.99 | 0.89, 1.09 | 0.738 |
| 1 HR = Hazard Ratio, CI = Confidence Interval | |||
c <- cox.zph(fit = model_3, transform ="km")
# Extract the ggcoxzph plots
plots <- ggcoxzph(c)[1:5] # Assuming you have 5 groups to plot
plots## $`1`
##
## $`2`
##
## $`3`
##
## $`4`
##
## $`5`
# Loop through the plots and save them
for (i in seq_along(plots)) {
# Set up the filename with the current date and letter identifiers
file_name <- paste0("Fig8-", LETTERS[i], "-Schoenfelds_residuals",Sys.Date(),".png")
full_path <- here(figures_output_dir, file_name)
# Save each plot
ggsave(
filename = full_path,
plot = plots[[i]],
width = 10,
height = 6,
units = "in",
dpi = 300
)
}Fig9-LGE-KM-LGE_extent
# Data parameters
results <- createSurvivalPlot(
data = df_LGE,
compared_with = "CMR_LGE_ischemic_extent_categ",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#4292C6","#FF002B","#2C3E50"),
my_legends = c("1-2 segments of ischemic LGE", "3-5 ischemic segments", "≥ 6 segments of ischemic"),
confint_choosen = 0.995,
mytitle = "Fig9-LGE-KM-LGE_extent"
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_ischemic_extent_categ"results$HR## HR_CI p_value
## CMR_LGE_ischemic_extent_categC_3_5_segments 8.92 ( 4.86 - 16.39 ) <0.001
## CMR_LGE_ischemic_extent_categD_more6_segments 68.33 ( 36.98 - 126.27 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig9-LGE-KM-LGE_extent_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig9-LGE-KM-LGE_extent_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig10-LGE-KM-LGE_transm
# Data parameters
results <- createSurvivalPlot(
data = df_LGE,
compared_with = "CMR_LGE_ischemic_transmurality",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#4292C6","#FF002B","#2C3E50"),
my_legends = c("Subendocardic <50%", "Subendocardic ≥ 50%", "Transmural"),
confint_choosen = 0.995,
mytitle = c("Fig10-LGE-KM-LGE_transm_risk")
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_ischemic_transmurality"results$HR## HR_CI
## CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% 3.57 ( 2.49 - 5.12 )
## CMR_LGE_ischemic_transmuralityD_Transmural 14.73 ( 10.17 - 21.34 )
## p_value
## CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% <0.001
## CMR_LGE_ischemic_transmuralityD_Transmural <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig10-LGE-KM-LGE_transm_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig10-LGE-KM-LGE_transm_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)KM LGE location
Fig11A-LGE-KM-LGE_loca6
# Data parameters
results <- createSurvivalPlot(
data = df_LGE,
compared_with = "CMR_LGE_ischemic_location_6",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#006400", "darkblue", "darkviolet", "#FF0000", "#8B4513", "#000000"),
my_legends = c("A_apical", "B_Inferior", "C_Lateral", "D_Anterior", "E_Septal", "Several_localization"),
confint_choosen = 0.995,
mytitle = c("Fig11A-LGE-KM-LGE_loca6"),
show.CI = FALSE
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_ischemic_location_6"results$HR## HR_CI
## CMR_LGE_ischemic_location_6Inferior 1.09 ( 0.29 - 4.00 )
## CMR_LGE_ischemic_location_6Lateral 2.50 ( 0.79 - 7.91 )
## CMR_LGE_ischemic_location_6Anterior 6.99 ( 2.16 - 22.60 )
## CMR_LGE_ischemic_location_6Septal 22.50 ( 7.30 - 69.32 )
## CMR_LGE_ischemic_location_6Several_localization 60.36 ( 22.18 - 164.26 )
## p_value
## CMR_LGE_ischemic_location_6Inferior 0.858
## CMR_LGE_ischemic_location_6Lateral 0.026
## CMR_LGE_ischemic_location_6Anterior <0.001
## CMR_LGE_ischemic_location_6Septal <0.001
## CMR_LGE_ischemic_location_6Several_localization <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig11A-LGE-KM-LGE_loca6_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig11A-LGE-KM-LGE_loca6_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig11B-LGE-KM-LGE_loca4
# Data parameters
results <- createSurvivalPlot(
data = df_LGE,
compared_with = "CMR_LGE_ischemic_location_4",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#4292C6","#FF002B","#2C3E50"),
my_legends = c("Other location", "Anterior", "Septal"),
confint_choosen = 0.995,
mytitle = c("Fig11B-LGE-KM-LGE_loca4")
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_ischemic_location_4"results$HR## HR_CI
## CMR_LGE_ischemic_location_4C_Anterior_without_septal 7.22 ( 4.87 - 10.70 )
## CMR_LGE_ischemic_location_4D_Septal 21.91 ( 15.90 - 30.18 )
## p_value
## CMR_LGE_ischemic_location_4C_Anterior_without_septal <0.001
## CMR_LGE_ischemic_location_4D_Septal <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig11B-LGE-KM-LGE_loca4_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig11B-LGE-KM-LGE_loca4_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig12-LGE-KM-LGE_midwall
# Data parameters
results <- createSurvivalPlot(
data = df_LGE,
compared_with = "CMR_LGE_midwall_presence",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("#FF002B","#2C3E50"),
my_legends = c("Ischemic LGE (excl)", "Isch and midwall LGE"),
confint_choosen = 0.995,
mytitle = c("Fig12-LGE-KM-LGE_midwall")
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ CMR_LGE_midwall_presence"results$HR## HR_CI p_value
## 1 3.13 ( 2.36 - 4.15 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig12-LGE-KM-LGE_midwall_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig12-LGE-KM-LGE_midwall_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig13-LGE-Stacked_Corr_var
library(ggplot2)
library(dplyr)
library(here)
# Function to create and save a ggplot
create_and_save_plot <- function(df, filename, title) {
gg <- ggplot(df, aes(x = IschemicExtent, y = Count, fill = Pattern)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("#009392","#f8766d","#7f7f7f")) +
labs(title = title, x = "Ischemic Extent", y = "Percentage", fill = "Pattern") +
theme_classic() +
theme(legend.position = "none")
print(gg)
ggsave(filename = here(figures_output_dir, filename), plot = gg, width = 5, height = 6, units = "in", dpi = 600)
}
# Droplevels to clean up factors in df_LGE
df_LGE <- df_LGE %>% droplevels()
# Function to prepare data
prepare_data <- function(category) {
table_data <- table(df_LGE$CMR_LGE_ischemic_extent_categ, category)
df <- as.data.frame(table_data)
names(df) <- c("IschemicExtent", "Pattern", "Count")
# Reverse the order of 'Pattern' factor levels
df$Pattern <- factor(df$Pattern, levels = rev(levels(df$Pattern)))
# Datafra
df <- df %>%
group_by(IschemicExtent) %>%
mutate(Percent = Count / sum(Count) * 100)
print(df)
return(df)
}
### By transmurality
df_transmurality <- prepare_data(df_LGE$CMR_LGE_ischemic_transmurality)## # A tibble: 9 × 4
## # Groups: IschemicExtent [3]
## IschemicExtent Pattern Count Percent
## <fct> <fct> <int> <dbl>
## 1 B_1_2_segments B_Subendocardial<50% 729 52.4
## 2 C_3_5_segments B_Subendocardial<50% 922 47.2
## 3 D_more6_segments B_Subendocardial<50% 47 19.1
## 4 B_1_2_segments C_Subendocardial≥50% 602 43.2
## 5 C_3_5_segments C_Subendocardial≥50% 842 43.1
## 6 D_more6_segments C_Subendocardial≥50% 105 42.7
## 7 B_1_2_segments D_Transmural 61 4.38
## 8 C_3_5_segments D_Transmural 189 9.68
## 9 D_more6_segments D_Transmural 94 38.2create_and_save_plot(df_transmurality, "Fig13A-LGE-Stacked_Corr_LGE_transmurality.png", "Percentage Stacked Histogram of LGE Characteristics by Ischemic Extent - Transmurality")
### By location
df_location <- prepare_data(df_LGE$CMR_LGE_ischemic_location_4)## # A tibble: 9 × 4
## # Groups: IschemicExtent [3]
## IschemicExtent Pattern Count Percent
## <fct> <fct> <int> <dbl>
## 1 B_1_2_segments B_Neither_anterior_nor_septal 1200 86.2
## 2 C_3_5_segments B_Neither_anterior_nor_septal 1425 73.0
## 3 D_more6_segments B_Neither_anterior_nor_septal 37 15.0
## 4 B_1_2_segments C_Anterior_without_septal 129 9.27
## 5 C_3_5_segments C_Anterior_without_septal 247 12.6
## 6 D_more6_segments C_Anterior_without_septal 46 18.7
## 7 B_1_2_segments D_Septal 63 4.53
## 8 C_3_5_segments D_Septal 281 14.4
## 9 D_more6_segments D_Septal 163 66.3create_and_save_plot(df_location, "Fig13B-LGE-Stacked_Corr_LGE_location.png", "Percentage Stacked Histogram of LGE Characteristics by Ischemic Extent - Location")
### By midwall
df_midwall <- prepare_data(df_LGE$CMR_LGE_midwall_presence)## # A tibble: 6 × 4
## # Groups: IschemicExtent [3]
## IschemicExtent Pattern Count Percent
## <fct> <fct> <int> <dbl>
## 1 B_1_2_segments A_No_midwall_LGE 1282 92.1
## 2 C_3_5_segments A_No_midwall_LGE 1774 90.8
## 3 D_more6_segments A_No_midwall_LGE 181 73.6
## 4 B_1_2_segments B_Presence_of_midwall_LGE 110 7.90
## 5 C_3_5_segments B_Presence_of_midwall_LGE 179 9.17
## 6 D_more6_segments B_Presence_of_midwall_LGE 65 26.4create_and_save_plot(df_midwall, "Fig13C-LGE-Stacked_Corr_LGE_midwall.png", "Percentage Stacked Histogram of LGE Characteristics by Ischemic Extent - Midwall")
# LGE population with LGE ≤6 segments (N=3,440) ## Survival ###
Tab12-univ-minus_LGE-death
df_selected <- df_LGE %>%
mutate(
surv.event = Surv(
time = outcome_FU_time_death,
event = outcome_death
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
) %>% filter(
CMR_LGE_ischemic_extent_count <= 6
)
Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
outcome_revascularisation_90days +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_presence_ischemic_and_midwall +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_midwall_extent_count +
CMR_LGE_midwall_extent_categ +
CMR_LGE_midwall_location_3 +
CMR_LGE_midwall_location_4 +
CMR_LGE_midwall_anterior +
CMR_LGE_midwall_septal +
CMR_LGE_midwall_inferior +
CMR_LGE_midwall_lateral +
CMR_LGE_midwall_apical,
data= df_selected,
method = 1, conf.level = 0.995),
hide.no = "No",
show.ratio=T, show.p.ratio = T)
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=3033 | N=407 | ||||
| demo_age | 64.1 (11.7) | 67.9 (11.5) | 1.03 [1.01;1.04] | <0.001 | <0.001 |
| demo_gender | 2186 (72.1%) | 368 (90.4%) | 3.33 [2.07;5.34] | <0.001 | <0.001 |
| demo_BMI | 27.7 (5.37) | 27.9 (5.71) | 1.01 [0.98;1.03] | 0.420 | 0.420 |
| CV_risk_diabete | 1134 (37.4%) | 224 (55.0%) | 1.90 [1.43;2.51] | <0.001 | <0.001 |
| CV_risk_HTA | 1626 (53.6%) | 210 (51.6%) | 0.93 [0.71;1.23] | 0.491 | 0.491 |
| CV_risk_obesity | 835 (27.5%) | 111 (27.3%) | 1.00 [0.73;1.37] | 0.979 | 0.977 |
| CV_risk_dyslipidemia | 1501 (49.5%) | 213 (52.3%) | 1.13 [0.86;1.49] | 0.215 | 0.213 |
| CV_risk_Smoking | 635 (20.9%) | 97 (23.8%) | 1.14 [0.83;1.59] | 0.247 | 0.248 |
| history_med_MI | 1162 (38.3%) | 158 (38.8%) | 1.02 [0.77;1.36] | 0.821 | 0.821 |
| history_coronary_procedure | 2670 (88.0%) | 364 (89.4%) | 1.13 [0.72;1.78] | 0.437 | 0.436 |
| history_interv_PCI | 196 (6.46%) | 43 (10.6%) | 1.56 [0.98;2.48] | 0.007 | 0.007 |
| history_interv_CABG | 2507 (82.7%) | 328 (80.6%) | 0.89 [0.62;1.27] | 0.360 | 0.360 |
| med_periph_atheroma | 156 (5.14%) | 40 (9.83%) | 1.90 [1.19;3.03] | <0.001 | <0.001 |
| history_stroke | 85 (2.80%) | 23 (5.65%) | 1.95 [1.07;3.56] | 0.002 | 0.002 |
| med_pacemaker | 21 (0.69%) | 2 (0.49%) | 0.74 [0.10;5.42] | 0.673 | 0.672 |
| med_CKD | 39 (1.29%) | 24 (5.90%) | 4.22 [2.34;7.63] | <0.001 | <0.001 |
| history_hospit_HF | 155 (5.11%) | 48 (11.8%) | 2.21 [1.43;3.40] | <0.001 | <0.001 |
| history_AFib | 207 (6.82%) | 35 (8.60%) | 1.25 [0.76;2.06] | 0.213 | 0.207 |
| clini_NYHA | 385 (12.7%) | 60 (14.7%) | 1.09 [0.74;1.61] | 0.543 | 0.541 |
| clini_cardiac_rythm | 197 (6.50%) | 31 (7.62%) | 1.15 [0.68;1.94] | 0.469 | 0.463 |
| outcome_revascularisation_90days | 2518 (83.0%) | 184 (45.2%) | 0.22 [0.16;0.29] | <0.001 | 0.000 |
| CMR_LVEF_5 | 8.79 (1.14) | 8.44 (1.28) | 0.83 [0.75;0.92] | <0.001 | <0.001 |
| CMR_LVEDV_5 | 10.2 (2.11) | 10.5 (2.36) | 1.04 [0.98;1.11] | 0.040 | 0.040 |
| CMR_LVESV_5 | 5.81 (1.74) | 6.16 (1.93) | 1.08 [1.01;1.16] | 0.001 | 0.001 |
| CMR_LV_mass | 91.1 (19.6) | 91.0 (19.1) | 1.00 [0.99;1.01] | 0.830 | 0.830 |
| CMR_RV_dysfunction | 89 (2.93%) | 39 (9.58%) | 3.09 [1.92;4.96] | <0.001 | <0.001 |
| CMR_LGE_presence_ischemic_and_midwall | 222 (7.32%) | 94 (23.1%) | 3.18 [2.28;4.43] | <0.001 | 0.000 |
| CMR_LGE_ischemic_presence: Presence_of_ischemic_LGE | 3033 (100%) | 407 (100%) | Ref. | Ref. | . |
| CMR_LGE_ischemic_extent_count | 2.72 (1.25) | 4.33 (1.27) | 2.24 [2.00;2.51] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| B_1_2_segments | 1369 (45.1%) | 23 (5.65%) | Ref. | Ref. | |
| C_3_5_segments | 1659 (54.7%) | 294 (72.2%) | 8.91 [4.85;16.4] | <0.001 | |
| D_more6_segments | 5 (0.16%) | 90 (22.1%) | 61.4 [31.8;119] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| B_Subendocardial<50% | 1613 (53.2%) | 52 (12.8%) | Ref. | Ref. | |
| C_Subendocardial≥50% | 1287 (42.4%) | 199 (48.9%) | 4.27 [2.76;6.62] | <0.001 | |
| D_Transmural | 133 (4.39%) | 156 (38.3%) | 18.8 [12.0;29.4] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| B_Neither_anterior_nor_septal | 2557 (84.3%) | 87 (21.4%) | Ref. | Ref. | |
| C_Anterior_without_septal | 316 (10.4%) | 74 (18.2%) | 5.99 [3.84;9.34] | <0.001 | |
| D_Septal | 160 (5.28%) | 246 (60.4%) | 21.6 [15.2;30.7] | <0.001 | |
| CMR_LGE_ischemic_anterior | 382 (12.6%) | 279 (68.6%) | 10.1 [7.48;13.6] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 160 (5.28%) | 246 (60.4%) | 13.3 [10.0;17.8] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 1093 (36.0%) | 89 (21.9%) | 0.54 [0.39;0.76] | <0.001 | <0.001 |
| CMR_LGE_ischemic_lateral | 962 (31.7%) | 158 (38.8%) | 1.27 [0.95;1.69] | 0.020 | 0.020 |
| CMR_LGE_ischemic_Apical | 872 (28.8%) | 214 (52.6%) | 2.44 [1.84;3.22] | <0.001 | 0.000 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 2811 (92.7%) | 313 (76.9%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 222 (7.32%) | 94 (23.1%) | 3.18 [2.28;4.43] | <0.001 | |
| CMR_LGE_midwall_extent_count | 0.08 (0.28) | 0.35 (0.70) | 2.52 [2.06;3.07] | <0.001 | <0.001 |
| CMR_LGE_midwall_extent_categ: | 0.000 | ||||
| None | 2811 (92.7%) | 313 (76.9%) | Ref. | Ref. | |
| Low_=1 | 212 (6.99%) | 49 (12.0%) | 2.04 [1.32;3.13] | <0.001 | |
| High_>1 | 10 (0.33%) | 45 (11.1%) | 8.51 [5.39;13.4] | <0.001 | |
| CMR_LGE_midwall_location_3: | 0.000 | ||||
| No_midwall_LGE | 2811 (92.7%) | 313 (76.9%) | Ref. | Ref. | |
| Midwall_LGE_not_at_risk | 178 (5.87%) | 36 (8.85%) | 1.67 [1.02;2.75] | 0.004 | |
| At_risk_midwall_LGE_(septal_and/or_lateral) | 44 (1.45%) | 58 (14.3%) | 7.00 [4.68;10.5] | <0.001 | |
| CMR_LGE_midwall_location_4: | 0.000 | ||||
| No_midwall_LGE | 2811 (92.7%) | 313 (76.9%) | Ref. | Ref. | |
| Midwall_LGE_not_at_risk | 178 (5.87%) | 36 (8.85%) | 1.67 [1.02;2.75] | 0.004 | |
| Lateral_midwall_LGE | 16 (0.53%) | 34 (8.35%) | 9.35 [5.62;15.5] | <0.001 | |
| Septal_Midwall_LGE | 28 (0.92%) | 24 (5.90%) | 5.16 [2.84;9.36] | <0.001 | |
| CMR_LGE_midwall_anterior | 82 (2.70%) | 13 (3.19%) | 1.14 [0.52;2.51] | 0.645 | 0.646 |
| CMR_LGE_midwall_septal | 28 (0.92%) | 24 (5.90%) | 4.54 [2.51;8.20] | <0.001 | <0.001 |
| CMR_LGE_midwall_inferior | 54 (1.78%) | 28 (6.88%) | 3.67 [2.12;6.36] | <0.001 | <0.001 |
| CMR_LGE_midwall_lateral | 16 (0.53%) | 39 (9.58%) | 8.74 [5.44;14.0] | <0.001 | 0.000 |
| CMR_LGE_midwall_apical | 43 (1.42%) | 9 (2.21%) | 1.28 [0.48;3.40] | 0.483 | 0.464 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab12-univ-minus_LGE-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab13-multi_tradi-minus_LGE-death
df <- df_LGE %>% filter(
CMR_LGE_ischemic_extent_count <= 6
) %>% mutate(
event = outcome_death,
time = outcome_FU_time_death,
CMR_LVEF_5 = CMR_LVEF/5
) %>% select(
event, time,
demo_age, demo_gender, demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF, history_AFib, med_CKD, history_med_MI , CMR_LVEF_5, CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_transmurality, CMR_LGE_ischemic_location_4, CMR_LGE_midwall_presence, outcome_revascularisation_90days) %>% droplevels()
#### TABLE
## Model 1
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LVEF_5))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 1", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.03 ( 0.02 - 0.04 ) | 1.03 ( 1.02 - 1.04 ) | <0.001 |
| demo_genderYes | 1.17 ( 0.69 - 1.64 ) | 3.21 ( 1.99 - 5.17 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.04 ) | 1.01 ( 0.98 - 1.04 ) | 0.397 |
| CV_risk_diabeteYes | 0.53 ( 0.24 - 0.83 ) | 1.71 ( 1.27 - 2.28 ) | <0.001 |
| CV_risk_SmokingYes | 0.31 ( -0.04 - 0.66 ) | 1.36 ( 0.96 - 1.93 ) | 0.013 |
| CV_risk_dyslipidemiaYes | 0.02 ( -0.26 - 0.31 ) | 1.02 ( 0.77 - 1.36 ) | 0.807 |
| history_hospit_HFYes | 0.48 ( 0.02 - 0.95 ) | 1.62 ( 1.02 - 2.58 ) | 0.003 |
| history_AFibYes | -0.19 ( -0.70 - 0.32 ) | 0.83 ( 0.50 - 1.38 ) | 0.302 |
| med_CKDYes | 1.10 ( 0.48 - 1.72 ) | 3.00 ( 1.62 - 5.57 ) | <0.001 |
| history_med_MIYes | -0.00 ( -0.30 - 0.29 ) | 1.00 ( 0.74 - 1.34 ) | 0.982 |
| CMR_LVEF_5 | -0.08 ( -0.19 - 0.03 ) | 0.93 ( 0.83 - 1.04 ) | 0.053 |
write.csv(data, file = here("outputs", "csv_files","minus_LGE_pop_model1_trad.csv"), append = FALSE)
## Model 2
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 2", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( 0.01 - 0.03 ) | 1.02 ( 1.01 - 1.03 ) | <0.001 |
| demo_genderYes | 0.99 ( 0.51 - 1.47 ) | 2.69 ( 1.67 - 4.35 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.04 ) | 1.01 ( 0.98 - 1.04 ) | 0.357 |
| CV_risk_diabeteYes | 0.42 ( 0.13 - 0.71 ) | 1.52 ( 1.14 - 2.03 ) | <0.001 |
| CV_risk_SmokingYes | 0.35 ( 0.00 - 0.71 ) | 1.43 ( 1.00 - 2.03 ) | 0.005 |
| CV_risk_dyslipidemiaYes | 0.02 ( -0.26 - 0.30 ) | 1.02 ( 0.77 - 1.35 ) | 0.852 |
| history_hospit_HFYes | 0.32 ( -0.16 - 0.79 ) | 1.37 ( 0.85 - 2.21 ) | 0.062 |
| history_AFibYes | -0.11 ( -0.63 - 0.41 ) | 0.90 ( 0.54 - 1.51 ) | 0.562 |
| med_CKDYes | 0.87 ( 0.26 - 1.49 ) | 2.40 ( 1.30 - 4.43 ) | <0.001 |
| history_med_MIYes | 0.02 ( -0.27 - 0.32 ) | 1.02 ( 0.76 - 1.38 ) | 0.832 |
| CMR_LVEF_5 | -0.05 ( -0.17 - 0.06 ) | 0.95 ( 0.84 - 1.07 ) | 0.207 |
| CMR_LGE_ischemic_extent_count | 0.73 ( 0.62 - 0.85 ) | 2.08 ( 1.86 - 2.33 ) | <0.001 |
## Model 3A
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_transmurality))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3A", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.00 - 0.03 ) | 1.01 ( 1.00 - 1.03 ) | 0.008 |
| demo_genderYes | 0.88 ( 0.40 - 1.36 ) | 2.41 ( 1.49 - 3.91 ) | <0.001 |
| demo_BMI | 0.00 ( -0.03 - 0.03 ) | 1.00 ( 0.97 - 1.03 ) | 0.833 |
| CV_risk_diabeteYes | 0.47 ( 0.17 - 0.76 ) | 1.59 ( 1.19 - 2.13 ) | <0.001 |
| CV_risk_SmokingYes | 0.21 ( -0.14 - 0.57 ) | 1.24 ( 0.87 - 1.76 ) | 0.089 |
| CV_risk_dyslipidemiaYes | 0.01 ( -0.27 - 0.30 ) | 1.01 ( 0.76 - 1.34 ) | 0.893 |
| history_hospit_HFYes | 0.18 ( -0.31 - 0.68 ) | 1.20 ( 0.73 - 1.97 ) | 0.300 |
| history_AFibYes | -0.35 ( -0.88 - 0.18 ) | 0.70 ( 0.41 - 1.19 ) | 0.061 |
| med_CKDYes | 0.88 ( 0.25 - 1.50 ) | 2.41 ( 1.29 - 4.50 ) | <0.001 |
| history_med_MIYes | 0.01 ( -0.29 - 0.31 ) | 1.01 ( 0.75 - 1.36 ) | 0.945 |
| CMR_LVEF_5 | -0.03 ( -0.15 - 0.09 ) | 0.97 ( 0.86 - 1.10 ) | 0.496 |
| CMR_LGE_ischemic_extent_count | 0.59 ( 0.48 - 0.70 ) | 1.81 ( 1.62 - 2.02 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 1.30 ( 0.85 - 1.74 ) | 3.65 ( 2.35 - 5.67 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 2.18 ( 1.71 - 2.65 ) | 8.83 ( 5.52 - 14.12 ) | <0.001 |
## Model 3B
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_location_4))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3B", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( 0.00 - 0.03 ) | 1.02 ( 1.00 - 1.03 ) | <0.001 |
| demo_genderYes | 0.75 ( 0.26 - 1.24 ) | 2.11 ( 1.30 - 3.44 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.04 ) | 1.01 ( 0.98 - 1.04 ) | 0.207 |
| CV_risk_diabeteYes | 0.41 ( 0.12 - 0.71 ) | 1.51 ( 1.13 - 2.03 ) | <0.001 |
| CV_risk_SmokingYes | 0.23 ( -0.13 - 0.59 ) | 1.26 ( 0.88 - 1.80 ) | 0.071 |
| CV_risk_dyslipidemiaYes | 0.07 ( -0.22 - 0.35 ) | 1.07 ( 0.81 - 1.42 ) | 0.511 |
| history_hospit_HFYes | 0.10 ( -0.40 - 0.59 ) | 1.10 ( 0.67 - 1.80 ) | 0.584 |
| history_AFibYes | -0.48 ( -1.00 - 0.04 ) | 0.62 ( 0.37 - 1.05 ) | 0.010 |
| med_CKDYes | 0.85 ( 0.22 - 1.48 ) | 2.34 ( 1.25 - 4.39 ) | <0.001 |
| history_med_MIYes | -0.09 ( -0.39 - 0.21 ) | 0.91 ( 0.68 - 1.23 ) | 0.390 |
| CMR_LVEF_5 | 0.00 ( -0.12 - 0.12 ) | 1.00 ( 0.89 - 1.13 ) | 0.967 |
| CMR_LGE_ischemic_extent_count | 0.42 ( 0.31 - 0.54 ) | 1.52 ( 1.36 - 1.71 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.54 ( 1.09 - 1.99 ) | 4.67 ( 2.97 - 7.32 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 2.40 ( 2.01 - 2.78 ) | 11.00 ( 7.48 - 16.16 ) | <0.001 |
## Model 3C
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3C", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( 0.00 - 0.03 ) | 1.02 ( 1.00 - 1.03 ) | <0.001 |
| demo_genderYes | 1.02 ( 0.54 - 1.50 ) | 2.77 ( 1.71 - 4.47 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.04 ) | 1.01 ( 0.98 - 1.04 ) | 0.526 |
| CV_risk_diabeteYes | 0.38 ( 0.09 - 0.67 ) | 1.46 ( 1.09 - 1.96 ) | <0.001 |
| CV_risk_SmokingYes | 0.40 ( 0.05 - 0.75 ) | 1.49 ( 1.05 - 2.12 ) | 0.001 |
| CV_risk_dyslipidemiaYes | 0.09 ( -0.19 - 0.38 ) | 1.10 ( 0.83 - 1.46 ) | 0.358 |
| history_hospit_HFYes | 0.38 ( -0.09 - 0.86 ) | 1.46 ( 0.91 - 2.35 ) | 0.024 |
| history_AFibYes | 0.01 ( -0.50 - 0.53 ) | 1.01 ( 0.60 - 1.70 ) | 0.948 |
| med_CKDYes | 0.84 ( 0.23 - 1.46 ) | 2.33 ( 1.26 - 4.28 ) | <0.001 |
| history_med_MIYes | 0.00 ( -0.30 - 0.30 ) | 1.00 ( 0.74 - 1.35 ) | 0.989 |
| CMR_LVEF_5 | -0.04 ( -0.15 - 0.08 ) | 0.96 ( 0.86 - 1.08 ) | 0.378 |
| CMR_LGE_ischemic_extent_count | 0.71 ( 0.60 - 0.83 ) | 2.04 ( 1.83 - 2.28 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.94 ( 0.60 - 1.28 ) | 2.56 ( 1.82 - 3.59 ) | <0.001 |
## Model 4
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.00 - 0.02 ) | 1.01 ( 1.00 - 1.02 ) | 0.034 |
| demo_genderYes | 0.66 ( 0.17 - 1.15 ) | 1.93 ( 1.18 - 3.16 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.04 ) | 1.01 ( 0.98 - 1.04 ) | 0.536 |
| CV_risk_diabeteYes | 0.42 ( 0.13 - 0.72 ) | 1.53 ( 1.14 - 2.05 ) | <0.001 |
| CV_risk_SmokingYes | 0.20 ( -0.16 - 0.56 ) | 1.22 ( 0.85 - 1.75 ) | 0.121 |
| CV_risk_dyslipidemiaYes | 0.11 ( -0.18 - 0.40 ) | 1.12 ( 0.84 - 1.49 ) | 0.283 |
| history_hospit_HFYes | 0.16 ( -0.35 - 0.66 ) | 1.17 ( 0.71 - 1.94 ) | 0.384 |
| history_AFibYes | -0.44 ( -0.96 - 0.09 ) | 0.64 ( 0.38 - 1.09 ) | 0.019 |
| med_CKDYes | 0.82 ( 0.19 - 1.45 ) | 2.27 ( 1.21 - 4.26 ) | <0.001 |
| history_med_MIYes | -0.10 ( -0.40 - 0.20 ) | 0.91 ( 0.67 - 1.23 ) | 0.371 |
| CMR_LVEF_5 | 0.03 ( -0.09 - 0.16 ) | 1.03 ( 0.91 - 1.17 ) | 0.448 |
| CMR_LGE_ischemic_extent_count | 0.36 ( 0.24 - 0.47 ) | 1.43 ( 1.27 - 1.60 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 1.03 ( 0.59 - 1.48 ) | 2.81 ( 1.80 - 4.41 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.43 ( 0.94 - 1.92 ) | 4.19 ( 2.56 - 6.84 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.49 ( 1.03 - 1.94 ) | 4.42 ( 2.81 - 6.96 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 2.06 ( 1.66 - 2.47 ) | 7.89 ( 5.27 - 11.80 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.59 ( 0.24 - 0.94 ) | 1.81 ( 1.27 - 2.57 ) | <0.001 |
write.csv(data, file = here("outputs", "csv_files","minus_LGE_pop_model4_trad.csv"), append = FALSE)
## Model 5
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: outcome_revascularisation_90days))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.01 - 0.02 ) | 1.01 ( 0.99 - 1.02 ) | 0.115 |
| demo_genderYes | 0.66 ( 0.17 - 1.15 ) | 1.93 ( 1.18 - 3.15 ) | <0.001 |
| demo_BMI | 0.01 ( -0.02 - 0.04 ) | 1.01 ( 0.98 - 1.04 ) | 0.480 |
| CV_risk_diabeteYes | 0.42 ( 0.13 - 0.72 ) | 1.53 ( 1.14 - 2.05 ) | <0.001 |
| CV_risk_SmokingYes | 0.18 ( -0.18 - 0.54 ) | 1.20 ( 0.84 - 1.72 ) | 0.153 |
| CV_risk_dyslipidemiaYes | 0.10 ( -0.19 - 0.39 ) | 1.10 ( 0.83 - 1.47 ) | 0.334 |
| history_hospit_HFYes | 0.15 ( -0.35 - 0.65 ) | 1.16 ( 0.70 - 1.92 ) | 0.401 |
| history_AFibYes | -0.41 ( -0.94 - 0.11 ) | 0.66 ( 0.39 - 1.12 ) | 0.028 |
| med_CKDYes | 0.81 ( 0.18 - 1.44 ) | 2.25 ( 1.20 - 4.23 ) | <0.001 |
| history_med_MIYes | -0.10 ( -0.40 - 0.20 ) | 0.90 ( 0.67 - 1.22 ) | 0.339 |
| CMR_LVEF_5 | 0.04 ( -0.09 - 0.16 ) | 1.04 ( 0.91 - 1.17 ) | 0.430 |
| CMR_LGE_ischemic_extent_count | 0.35 ( 0.24 - 0.47 ) | 1.42 ( 1.27 - 1.60 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 1.01 ( 0.56 - 1.46 ) | 2.75 ( 1.75 - 4.31 ) | <0.001 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.20 ( 0.64 - 1.77 ) | 3.34 ( 1.90 - 5.86 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.47 ( 1.02 - 1.93 ) | 4.37 ( 2.78 - 6.88 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 2.05 ( 1.65 - 2.45 ) | 7.77 ( 5.19 - 11.63 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.58 ( 0.23 - 0.93 ) | 1.79 ( 1.26 - 2.54 ) | <0.001 |
| outcome_revascularisation_90daysYes | -0.31 ( -0.71 - 0.08 ) | 0.73 ( 0.49 - 1.09 ) | 0.026 |
## MODELS MULTI NESTED
var_base = c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5")
### Nested 4
table <- uni_multi_function(
df = df,
event = "event",
time = "time",
var_base = var_base,
var_added = c("CMR_LGE_ischemic_extent_count", "CMR_LGE_ischemic_transmurality","CMR_LGE_ischemic_location_4",
"CMR_LGE_midwall_presence", "outcome_revascularisation_90days"))
# export in html
knitr::kable(table, caption = "Model full - granularity until revasc (minus LGE pop = 3,440)", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Variable | Univariate_Analysis | model_multi_1 | model_multi_2 | model_multi_3 | model_multi_4 | model_multi_5 | model_multi_6 |
|---|---|---|---|---|---|---|---|
| demo_age | 1.03 (1.01-1.04), p=0.000 | 1.03 (1.02-1.04), p=0.000 | 1.02 (1.01-1.03), p=0.000 | 1.01 (1.00-1.03), p=0.008 | 1.01 (1.00-1.03), p=0.016 | 1.01 (1.00-1.02), p=0.034 | 1.01 (0.99-1.02), p=0.115 |
| demo_genderYes | 3.33 (2.07-5.34), p=0.000 | 3.21 (1.99-5.17), p=0.000 | 2.69 (1.67-4.35), p=0.000 | 2.41 (1.49-3.91), p=0.000 | 1.93 (1.18-3.16), p=0.000 | 1.93 (1.18-3.16), p=0.000 | 1.93 (1.18-3.15), p=0.000 |
| demo_BMI | 1.01 (0.98-1.03), p=0.420 | 1.01 (0.98-1.04), p=0.397 | 1.01 (0.98-1.04), p=0.357 | 1.00 (0.97-1.03), p=0.833 | 1.01 (0.98-1.04), p=0.433 | 1.01 (0.98-1.04), p=0.536 | 1.01 (0.98-1.04), p=0.480 |
| CV_risk_diabeteYes | 1.90 (1.43-2.51), p=0.000 | 1.71 (1.27-2.28), p=0.000 | 1.52 (1.14-2.03), p=0.000 | 1.59 (1.19-2.13), p=0.000 | 1.57 (1.17-2.11), p=0.000 | 1.53 (1.14-2.05), p=0.000 | 1.53 (1.14-2.05), p=0.000 |
| CV_risk_SmokingYes | 1.14 (0.83-1.59), p=0.247 | 1.36 (0.96-1.93), p=0.013 | 1.43 (1.00-2.03), p=0.005 | 1.24 (0.87-1.76), p=0.089 | 1.16 (0.81-1.66), p=0.243 | 1.22 (0.85-1.75), p=0.121 | 1.20 (0.84-1.72), p=0.153 |
| CV_risk_dyslipidemiaYes | 1.13 (0.86-1.49), p=0.215 | 1.02 (0.77-1.36), p=0.807 | 1.02 (0.77-1.35), p=0.852 | 1.01 (0.76-1.34), p=0.893 | 1.04 (0.78-1.38), p=0.697 | 1.12 (0.84-1.49), p=0.283 | 1.10 (0.83-1.47), p=0.334 |
| history_hospit_HFYes | 2.21 (1.43-3.40), p=0.000 | 1.62 (1.02-2.58), p=0.003 | 1.37 (0.85-2.21), p=0.062 | 1.20 (0.73-1.97), p=0.300 | 1.04 (0.62-1.72), p=0.843 | 1.17 (0.71-1.94), p=0.384 | 1.16 (0.70-1.92), p=0.401 |
| history_AFibYes | 1.25 (0.76-2.06), p=0.213 | 0.83 (0.50-1.38), p=0.302 | 0.90 (0.54-1.51), p=0.562 | 0.70 (0.41-1.19), p=0.061 | 0.58 (0.35-0.99), p=0.004 | 0.64 (0.38-1.09), p=0.019 | 0.66 (0.39-1.12), p=0.028 |
| med_CKDYes | 4.22 (2.34-7.63), p=0.000 | 3.00 (1.62-5.57), p=0.000 | 2.40 (1.30-4.43), p=0.000 | 2.41 (1.29-4.50), p=0.000 | 2.29 (1.22-4.31), p=0.000 | 2.27 (1.21-4.26), p=0.000 | 2.25 (1.20-4.23), p=0.000 |
| history_med_MIYes | 1.02 (0.77-1.36), p=0.821 | 1.00 (0.74-1.34), p=0.982 | 1.02 (0.76-1.38), p=0.832 | 1.01 (0.75-1.36), p=0.945 | 0.93 (0.69-1.26), p=0.518 | 0.91 (0.67-1.23), p=0.371 | 0.90 (0.67-1.22), p=0.339 |
| CMR_LVEF_5 | 0.83 (0.75-0.92), p=0.000 | 0.93 (0.83-1.04), p=0.053 | 0.95 (0.84-1.07), p=0.207 | 0.97 (0.86-1.10), p=0.496 | 1.02 (0.90-1.16), p=0.679 | 1.03 (0.91-1.17), p=0.448 | 1.04 (0.91-1.17), p=0.430 |
| CMR_LGE_ischemic_extent_count | 2.24 (2.00-2.51), p=0.000 | NA | 2.08 (1.86-2.33), p=0.000 | 1.81 (1.62-2.02), p=0.000 | 1.43 (1.28-1.61), p=0.000 | 1.43 (1.27-1.60), p=0.000 | 1.42 (1.27-1.60), p=0.000 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 4.27 (2.76-6.62), p=0.000 | NA | NA | 3.65 (2.35-5.67), p=0.000 | 2.94 (1.88-4.60), p=0.000 | 2.81 (1.80-4.41), p=0.000 | 2.75 (1.75-4.31), p=0.000 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 18.76 (11.96-29.43), p=0.000 | NA | NA | 8.83 (5.52-14.12), p=0.000 | 4.57 (2.81-7.44), p=0.000 | 4.19 (2.56-6.84), p=0.000 | 3.34 (1.90-5.86), p=0.000 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 5.99 (3.84-9.34), p=0.000 | NA | NA | NA | 4.41 (2.81-6.94), p=0.000 | 4.42 (2.81-6.96), p=0.000 | 4.37 (2.78-6.88), p=0.000 |
| CMR_LGE_ischemic_location_4D_Septal | 21.63 (15.23-30.72), p=0.000 | NA | NA | NA | 8.08 (5.41-12.09), p=0.000 | 7.89 (5.27-11.80), p=0.000 | 7.77 (5.19-11.63), p=0.000 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 3.18 (2.28-4.43), p=0.000 | NA | NA | NA | NA | 1.81 (1.27-2.57), p=0.000 | 1.79 (1.26-2.54), p=0.000 |
| outcome_revascularisation_90daysYes | 0.22 (0.16-0.29), p=0.000 | NA | NA | NA | NA | NA | 0.73 (0.49-1.09), p=0.026 |
# Export in word
ft <- flextable(table)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Model 4 - LGE pop (N=3,440", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab13-multi_tradi-minus_LGE-death-",Sys.Date(), ".docx")))Tab14-incr-minus_LGE-death
var1 <- c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5")
var2 <- c(var1, "CMR_LGE_ischemic_extent_count")
var3A <- c(var2, "CMR_LGE_ischemic_transmurality")
var3B <- c(var2, "CMR_LGE_ischemic_location_4")
var3C <- c(var2, "CMR_LGE_midwall_presence")
var4 <- c(var2, "CMR_LGE_ischemic_transmurality", "CMR_LGE_ischemic_location_4", "CMR_LGE_midwall_presence")
df_LGE_minus <- df_LGE %>% filter(CMR_LGE_ischemic_extent_count <= 6) %>% mutate(
time = outcome_FU_time_death,
event = outcome_death,
CMR_LVEF = CMR_LVEF/5
)%>% droplevels()
# MODEL 1
results.model1 <- CoxPredictScore(derivation = df_LGE_minus, validation = df_LGE_minus,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var1,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model1$model_stats## NULLresults.model1$derivation$auc## [1] "0.68 [0.64-0.72]"# MODEL 2
results.model2 <- CoxPredictScore(derivation = df_LGE_minus, validation = df_LGE_minus,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var2,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model2$model_stats## NULLresults.model2$derivation$auc## [1] "0.8 [0.75-0.84]"roc.test(results.model1$roc_derivation$roc_obj, results.model2$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model1$roc_derivation$roc_obj and results.model2$roc_derivation$roc_obj
## Z = -7.3726, p-value = 1.674e-13
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.15163774 -0.08794567
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.6810355 0.8008272# MODEL 3A
results.model3A <- CoxPredictScore(derivation = df_LGE_minus, validation = df_LGE_minus,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3A,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3A$model_stats## NULLresults.model3A$derivation$auc## [1] "0.85 [0.83-0.88]"roc.test(results.model2$roc_derivation$roc_obj, results.model3A$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3A$roc_derivation$roc_obj
## Z = -4.9818, p-value = 6.298e-07
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.06988273 -0.03042105
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8008272 0.8509791# MODEL 3B
results.model3B <- CoxPredictScore(derivation = df_LGE_minus, validation = df_LGE_minus,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3B,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3B$model_stats## NULLresults.model3B$derivation$auc## [1] "0.88 [0.86-0.91]"roc.test(results.model2$roc_derivation$roc_obj, results.model3B$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3B$roc_derivation$roc_obj
## Z = -6.9694, p-value = 3.182e-12
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.10112977 -0.05673473
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8008272 0.8797594# MODEL 3C
results.model3C <- CoxPredictScore(derivation = df_LGE_minus, validation = df_LGE_minus,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3C,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3C$model_stats## NULLresults.model3C$derivation$auc## [1] "0.81 [0.77-0.85]"roc.test(results.model2$roc_derivation$roc_obj, results.model3C$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3C$roc_derivation$roc_obj
## Z = -2.5202, p-value = 0.01173
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.020323540 -0.002541511
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8008272 0.8122597# MODEL 4
results.model4 <- CoxPredictScore(derivation = df_LGE_minus, validation = df_LGE_minus,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var4,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model4$model_stats## NULLresults.model4$derivation$auc## [1] "0.9 [0.88-0.92]"roc.test(results.model2$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -7.9664, p-value = 1.634e-15
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.12198186 -0.07381117
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8008272 0.8987237MACE population (N=1,500)
Descriptive
Tab15-descr-MACEpop-death
df_selected <- df_MACE %>%
mutate(
outcome_death = as.factor(outcome_death),
outcome_MACE = as.factor(outcome_MACE),
outcome_MACE_CV_death = as.factor(outcome_MACE_CV_death),
outcome_MACE_NonFatal_MI = as.factor(outcome_MACE_NonFatal_MI),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
outcome_death ~
outcome_death + outcome_MACE + outcome_MACE_CV_death + outcome_MACE_NonFatal_MI +
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_presence_ischemic_and_midwall +
outcome_revascularisation_90days,
data= df_selected,
method = 1, conf.level = 0.995), # Pvalue
hide.no = "No", show.all = TRUE, show.p.overall = TRUE) # bug avec le hide
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| [ALL] | 0 | 1 | p.overall | |
|---|---|---|---|---|
| N=1500 | N=1273 | N=227 | ||
| outcome_death: | 0.000 | |||
| 0 | 1273 (84.9%) | 1273 (100%) | 0 (0.00%) | |
| 1 | 227 (15.1%) | 0 (0.00%) | 227 (100%) | |
| outcome_MACE: | <0.001 | |||
| 0 | 1227 (81.8%) | 1207 (94.8%) | 20 (8.81%) | |
| 1 | 273 (18.2%) | 66 (5.18%) | 207 (91.2%) | |
| outcome_MACE_CV_death: | <0.001 | |||
| 0 | 1286 (86.5%) | 1269 (100%) | 17 (7.83%) | |
| 1 | 200 (13.5%) | 0 (0.00%) | 200 (92.2%) | |
| outcome_MACE_NonFatal_MI: | 0.302 | |||
| 0 | 1414 (95.2%) | 1204 (94.9%) | 210 (96.8%) | |
| 1 | 72 (4.85%) | 65 (5.12%) | 7 (3.23%) | |
| demo_age | 64.2 (11.8) | 63.3 (11.7) | 69.1 (11.2) | <0.001 |
| demo_gender | 1123 (74.9%) | 924 (72.6%) | 199 (87.7%) | <0.001 |
| demo_BMI | 27.7 (5.47) | 27.7 (5.52) | 27.6 (5.20) | 0.889 |
| CV_risk_diabete | 547 (36.5%) | 417 (32.8%) | 130 (57.3%) | <0.001 |
| CV_risk_HTA | 650 (43.3%) | 534 (41.9%) | 116 (51.1%) | 0.013 |
| CV_risk_obesity | 407 (27.1%) | 343 (26.9%) | 64 (28.2%) | 0.757 |
| CV_risk_dyslipidemia | 708 (47.2%) | 598 (47.0%) | 110 (48.5%) | 0.734 |
| CV_risk_Smoking | 265 (17.7%) | 222 (17.4%) | 43 (18.9%) | 0.651 |
| history_med_MI | 573 (38.2%) | 491 (38.6%) | 82 (36.1%) | 0.532 |
| history_coronary_procedure | 1328 (88.5%) | 1129 (88.7%) | 199 (87.7%) | 0.739 |
| history_interv_PCI | 105 (7.00%) | 79 (6.21%) | 26 (11.5%) | 0.007 |
| history_interv_CABG | 1240 (82.7%) | 1066 (83.7%) | 174 (76.7%) | 0.012 |
| med_periph_atheroma | 86 (5.73%) | 58 (4.56%) | 28 (12.3%) | <0.001 |
| history_stroke | 50 (3.33%) | 36 (2.83%) | 14 (6.17%) | 0.017 |
| med_pacemaker | 16 (1.07%) | 16 (1.26%) | 0 (0.00%) | 0.152 |
| med_CKD | 34 (2.27%) | 23 (1.81%) | 11 (4.85%) | 0.010 |
| history_hospit_HF | 95 (6.33%) | 72 (5.66%) | 23 (10.1%) | 0.016 |
| history_AFib | 128 (8.53%) | 95 (7.46%) | 33 (14.5%) | 0.001 |
| clini_NYHA | 198 (13.2%) | 172 (13.5%) | 26 (11.5%) | 0.461 |
| clini_cardiac_rythm | 124 (8.27%) | 95 (7.46%) | 29 (12.8%) | 0.011 |
| CMR_LVEF_5 | 8.74 (1.17) | 8.79 (1.16) | 8.45 (1.21) | <0.001 |
| CMR_LVEDV_5 | 10.3 (2.21) | 10.3 (2.17) | 10.7 (2.43) | 0.022 |
| CMR_LVESV_5 | 5.89 (1.83) | 5.83 (1.80) | 6.24 (1.95) | 0.004 |
| CMR_LV_mass | 90.3 (20.2) | 89.9 (20.3) | 92.3 (19.4) | 0.090 |
| CMR_RV_dysfunction | 60 (4.00%) | 33 (2.59%) | 27 (11.9%) | <0.001 |
| CMR_LGE_ischemic_presence: | <0.001 | |||
| No_ischemic_LGE | 703 (46.9%) | 659 (51.8%) | 44 (19.4%) | |
| Presence_of_ischemic_LGE | 797 (53.1%) | 614 (48.2%) | 183 (80.6%) | |
| CMR_LGE_ischemic_extent_count | 1.77 (2.11) | 1.32 (1.64) | 4.30 (2.63) | <0.001 |
| CMR_LGE_ischemic_extent_categ: | <0.001 | |||
| A_No_ischemic_LGE | 703 (46.9%) | 659 (51.8%) | 44 (19.4%) | |
| B_1_2_segments | 284 (18.9%) | 276 (21.7%) | 8 (3.52%) | |
| C_3_5_segments | 418 (27.9%) | 335 (26.3%) | 83 (36.6%) | |
| D_more6_segments | 95 (6.33%) | 3 (0.24%) | 92 (40.5%) | |
| CMR_LGE_ischemic_transmurality: | <0.001 | |||
| A_No_ischemic_LGE | 703 (46.9%) | 659 (51.8%) | 44 (19.4%) | |
| B_Subendocardial<50% | 358 (23.9%) | 332 (26.1%) | 26 (11.5%) | |
| C_Subendocardial≥50% | 341 (22.7%) | 257 (20.2%) | 84 (37.0%) | |
| D_Transmural | 98 (6.53%) | 25 (1.96%) | 73 (32.2%) | |
| CMR_LGE_ischemic_location_4: | <0.001 | |||
| A_No_ischemic_LGE | 703 (46.9%) | 659 (51.8%) | 44 (19.4%) | |
| B_Neither_anterior_nor_septal | 535 (35.7%) | 524 (41.2%) | 11 (4.85%) | |
| C_Anterior_without_septal | 82 (5.47%) | 60 (4.71%) | 22 (9.69%) | |
| D_Septal | 180 (12.0%) | 30 (2.36%) | 150 (66.1%) | |
| CMR_LGE_ischemic_anterior | 222 (14.8%) | 72 (5.66%) | 150 (66.1%) | <0.001 |
| CMR_LGE_ischemic_septal | 180 (12.0%) | 30 (2.36%) | 150 (66.1%) | <0.001 |
| CMR_LGE_ischemic_inferior | 284 (18.9%) | 257 (20.2%) | 27 (11.9%) | 0.004 |
| CMR_LGE_ischemic_lateral | 261 (17.4%) | 189 (14.8%) | 72 (31.7%) | <0.001 |
| CMR_LGE_ischemic_Apical | 282 (18.8%) | 182 (14.3%) | 100 (44.1%) | <0.001 |
| CMR_LGE_midwall_presence: | <0.001 | |||
| A_No_midwall_LGE | 1320 (88.0%) | 1196 (94.0%) | 124 (54.6%) | |
| B_Presence_of_midwall_LGE | 180 (12.0%) | 77 (6.05%) | 103 (45.4%) | |
| CMR_LGE_presence_ischemic_and_midwall | 115 (7.67%) | 38 (2.99%) | 77 (33.9%) | <0.001 |
| outcome_revascularisation_90days | 596 (39.7%) | 512 (40.2%) | 84 (37.0%) | 0.402 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab15-descr-MACEpop-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab16-descr-MACEpop-MACE
df_selected <- df_MACE %>%
mutate(
outcome_death = as.factor(outcome_death),
outcome_MACE = as.factor(outcome_MACE),
outcome_MACE_CV_death = as.factor(outcome_MACE_CV_death),
outcome_MACE_NonFatal_MI = as.factor(outcome_MACE_NonFatal_MI),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
outcome_MACE ~
outcome_death + outcome_MACE + outcome_MACE_CV_death + outcome_MACE_NonFatal_MI +
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_presence_ischemic_and_midwall +
outcome_revascularisation_90days,
data= df_selected,
method = 1, conf.level = 0.995), # Pvalue
hide.no = "No", show.all = TRUE, show.p.overall = TRUE) # bug avec le hide
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| [ALL] | 0 | 1 | p.overall | |
|---|---|---|---|---|
| N=1500 | N=1227 | N=273 | ||
| outcome_death: | <0.001 | |||
| 0 | 1273 (84.9%) | 1207 (98.4%) | 66 (24.2%) | |
| 1 | 227 (15.1%) | 20 (1.63%) | 207 (75.8%) | |
| outcome_MACE: | 0.000 | |||
| 0 | 1227 (81.8%) | 1227 (100%) | 0 (0.00%) | |
| 1 | 273 (18.2%) | 0 (0.00%) | 273 (100%) | |
| outcome_MACE_CV_death: | <0.001 | |||
| 0 | 1286 (86.5%) | 1213 (100%) | 73 (26.7%) | |
| 1 | 200 (13.5%) | 0 (0.00%) | 200 (73.3%) | |
| outcome_MACE_NonFatal_MI: | <0.001 | |||
| 0 | 1414 (95.2%) | 1213 (100%) | 201 (73.6%) | |
| 1 | 72 (4.85%) | 0 (0.00%) | 72 (26.4%) | |
| demo_age | 64.2 (11.8) | 63.3 (11.8) | 68.1 (11.4) | <0.001 |
| demo_gender | 1123 (74.9%) | 892 (72.7%) | 231 (84.6%) | <0.001 |
| demo_BMI | 27.7 (5.47) | 27.7 (5.58) | 27.5 (4.95) | 0.455 |
| CV_risk_diabete | 547 (36.5%) | 410 (33.4%) | 137 (50.2%) | <0.001 |
| CV_risk_HTA | 650 (43.3%) | 514 (41.9%) | 136 (49.8%) | 0.020 |
| CV_risk_obesity | 407 (27.1%) | 336 (27.4%) | 71 (26.0%) | 0.698 |
| CV_risk_dyslipidemia | 708 (47.2%) | 580 (47.3%) | 128 (46.9%) | 0.962 |
| CV_risk_Smoking | 265 (17.7%) | 215 (17.5%) | 50 (18.3%) | 0.824 |
| history_med_MI | 573 (38.2%) | 473 (38.5%) | 100 (36.6%) | 0.602 |
| history_coronary_procedure | 1328 (88.5%) | 1087 (88.6%) | 241 (88.3%) | 0.967 |
| history_interv_PCI | 105 (7.00%) | 79 (6.44%) | 26 (9.52%) | 0.094 |
| history_interv_CABG | 1240 (82.7%) | 1024 (83.5%) | 216 (79.1%) | 0.105 |
| med_periph_atheroma | 86 (5.73%) | 58 (4.73%) | 28 (10.3%) | 0.001 |
| history_stroke | 50 (3.33%) | 35 (2.85%) | 15 (5.49%) | 0.044 |
| med_pacemaker | 16 (1.07%) | 13 (1.06%) | 3 (1.10%) | 1.000 |
| med_CKD | 34 (2.27%) | 23 (1.87%) | 11 (4.03%) | 0.053 |
| history_hospit_HF | 95 (6.33%) | 71 (5.79%) | 24 (8.79%) | 0.088 |
| history_AFib | 128 (8.53%) | 87 (7.09%) | 41 (15.0%) | <0.001 |
| clini_NYHA | 198 (13.2%) | 162 (13.2%) | 36 (13.2%) | 1.000 |
| clini_cardiac_rythm | 124 (8.27%) | 86 (7.01%) | 38 (13.9%) | <0.001 |
| CMR_LVEF_5 | 8.74 (1.17) | 8.78 (1.16) | 8.53 (1.18) | 0.001 |
| CMR_LVEDV_5 | 10.3 (2.21) | 10.3 (2.17) | 10.6 (2.38) | 0.038 |
| CMR_LVESV_5 | 5.89 (1.83) | 5.83 (1.81) | 6.16 (1.92) | 0.011 |
| CMR_LV_mass | 90.3 (20.2) | 89.8 (20.2) | 92.2 (20.1) | 0.075 |
| CMR_RV_dysfunction | 60 (4.00%) | 28 (2.28%) | 32 (11.7%) | <0.001 |
| CMR_LGE_ischemic_presence: | <0.001 | |||
| No_ischemic_LGE | 703 (46.9%) | 634 (51.7%) | 69 (25.3%) | |
| Presence_of_ischemic_LGE | 797 (53.1%) | 593 (48.3%) | 204 (74.7%) | |
| CMR_LGE_ischemic_extent_count | 1.77 (2.11) | 1.31 (1.64) | 3.86 (2.66) | <0.001 |
| CMR_LGE_ischemic_extent_categ: | <0.001 | |||
| A_No_ischemic_LGE | 703 (46.9%) | 634 (51.7%) | 69 (25.3%) | |
| B_1_2_segments | 284 (18.9%) | 275 (22.4%) | 9 (3.30%) | |
| C_3_5_segments | 418 (27.9%) | 307 (25.0%) | 111 (40.7%) | |
| D_more6_segments | 95 (6.33%) | 11 (0.90%) | 84 (30.8%) | |
| CMR_LGE_ischemic_transmurality: | <0.001 | |||
| A_No_ischemic_LGE | 703 (46.9%) | 634 (51.7%) | 69 (25.3%) | |
| B_Subendocardial<50% | 358 (23.9%) | 319 (26.0%) | 39 (14.3%) | |
| C_Subendocardial≥50% | 341 (22.7%) | 248 (20.2%) | 93 (34.1%) | |
| D_Transmural | 98 (6.53%) | 26 (2.12%) | 72 (26.4%) | |
| CMR_LGE_ischemic_location_4: | <0.001 | |||
| A_No_ischemic_LGE | 703 (46.9%) | 634 (51.7%) | 69 (25.3%) | |
| B_Neither_anterior_nor_septal | 535 (35.7%) | 503 (41.0%) | 32 (11.7%) | |
| C_Anterior_without_septal | 82 (5.47%) | 63 (5.13%) | 19 (6.96%) | |
| D_Septal | 180 (12.0%) | 27 (2.20%) | 153 (56.0%) | |
| CMR_LGE_ischemic_anterior | 222 (14.8%) | 72 (5.87%) | 150 (54.9%) | <0.001 |
| CMR_LGE_ischemic_septal | 180 (12.0%) | 27 (2.20%) | 153 (56.0%) | <0.001 |
| CMR_LGE_ischemic_inferior | 284 (18.9%) | 234 (19.1%) | 50 (18.3%) | 0.839 |
| CMR_LGE_ischemic_lateral | 261 (17.4%) | 173 (14.1%) | 88 (32.2%) | <0.001 |
| CMR_LGE_ischemic_Apical | 282 (18.8%) | 179 (14.6%) | 103 (37.7%) | <0.001 |
| CMR_LGE_midwall_presence: | <0.001 | |||
| A_No_midwall_LGE | 1320 (88.0%) | 1141 (93.0%) | 179 (65.6%) | |
| B_Presence_of_midwall_LGE | 180 (12.0%) | 86 (7.01%) | 94 (34.4%) | |
| CMR_LGE_presence_ischemic_and_midwall | 115 (7.67%) | 43 (3.50%) | 72 (26.4%) | <0.001 |
| outcome_revascularisation_90days | 596 (39.7%) | 490 (39.9%) | 106 (38.8%) | 0.787 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab16-descr-MACEpop-MACE-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Survival
Tab17-univ-MACEpop-death
df_selected <- df_MACE %>%
mutate(
surv.event = Surv(
time = outcome_FU_time_death,
event = outcome_death
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_presence_ischemic_and_midwall +
outcome_revascularisation_90days,
data= df_selected,
method = 1, conf.level = 0.995), # Pvalue
hide.no = "No", show.p.ratio = T, show.ratio = TRUE) # bug avec le hide
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=1273 | N=227 | ||||
| demo_age | 63.3 (11.7) | 69.1 (11.2) | 1.04 [1.02;1.06] | <0.001 | <0.001 |
| demo_gender | 924 (72.6%) | 199 (87.7%) | 2.53 [1.43;4.46] | <0.001 | <0.001 |
| demo_BMI | 27.7 (5.52) | 27.6 (5.20) | 1.00 [0.97;1.04] | 0.979 | 0.979 |
| CV_risk_diabete | 417 (32.8%) | 130 (57.3%) | 2.51 [1.72;3.65] | <0.001 | <0.001 |
| CV_risk_HTA | 534 (41.9%) | 116 (51.1%) | 1.41 [0.97;2.05] | 0.010 | 0.010 |
| CV_risk_obesity | 343 (26.9%) | 64 (28.2%) | 1.08 [0.71;1.63] | 0.610 | 0.605 |
| CV_risk_dyslipidemia | 598 (47.0%) | 110 (48.5%) | 1.16 [0.80;1.68] | 0.274 | 0.273 |
| CV_risk_Smoking | 222 (17.4%) | 43 (18.9%) | 1.12 [0.69;1.80] | 0.517 | 0.514 |
| history_med_MI | 491 (38.6%) | 82 (36.1%) | 0.94 [0.64;1.38] | 0.649 | 0.647 |
| history_coronary_procedure | 1129 (88.7%) | 199 (87.7%) | 0.93 [0.53;1.63] | 0.705 | 0.706 |
| history_interv_PCI | 79 (6.21%) | 26 (11.5%) | 1.78 [0.97;3.26] | 0.008 | 0.007 |
| history_interv_CABG | 1066 (83.7%) | 174 (76.7%) | 0.69 [0.44;1.07] | 0.018 | 0.018 |
| med_periph_atheroma | 58 (4.56%) | 28 (12.3%) | 2.81 [1.59;4.96] | <0.001 | <0.001 |
| history_stroke | 36 (2.83%) | 14 (6.17%) | 1.88 [0.85;4.17] | 0.026 | 0.019 |
| med_pacemaker | 16 (1.26%) | 0 (0.00%) | 0.00 [0.00;.] | 0.989 | 0.099 |
| med_CKD | 23 (1.81%) | 11 (4.85%) | 2.93 [1.23;6.99] | 0.001 | <0.001 |
| history_hospit_HF | 72 (5.66%) | 23 (10.1%) | 1.54 [0.83;2.85] | 0.051 | 0.049 |
| history_AFib | 95 (7.46%) | 33 (14.5%) | 2.12 [1.25;3.60] | <0.001 | <0.001 |
| clini_NYHA | 172 (13.5%) | 26 (11.5%) | 0.76 [0.42;1.36] | 0.179 | 0.178 |
| clini_cardiac_rythm | 95 (7.46%) | 29 (12.8%) | 1.90 [1.08;3.32] | 0.001 | 0.001 |
| CMR_LVEF_5 | 8.79 (1.16) | 8.45 (1.21) | 0.83 [0.72;0.95] | <0.001 | <0.001 |
| CMR_LVEDV_5 | 10.3 (2.17) | 10.7 (2.43) | 1.07 [0.99;1.16] | 0.012 | 0.012 |
| CMR_LVESV_5 | 5.83 (1.80) | 6.24 (1.95) | 1.10 [1.01;1.20] | 0.001 | 0.001 |
| CMR_LV_mass | 89.9 (20.3) | 92.3 (19.4) | 1.01 [1.00;1.02] | 0.028 | 0.028 |
| CMR_RV_dysfunction | 33 (2.59%) | 27 (11.9%) | 3.84 [2.16;6.84] | <0.001 | <0.001 |
| CMR_LGE_ischemic_presence: | 0.000 | ||||
| No_ischemic_LGE | 659 (51.8%) | 44 (19.4%) | Ref. | Ref. | |
| Presence_of_ischemic_LGE | 614 (48.2%) | 183 (80.6%) | 3.84 [2.40;6.16] | <0.001 | |
| CMR_LGE_ischemic_extent_count | 1.32 (1.64) | 4.30 (2.63) | 1.61 [1.49;1.73] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| A_No_ischemic_LGE | 659 (51.8%) | 44 (19.4%) | Ref. | Ref. | |
| B_1_2_segments | 276 (21.7%) | 8 (3.52%) | 0.44 [0.15;1.30] | 0.034 | |
| C_3_5_segments | 335 (26.3%) | 83 (36.6%) | 3.35 [1.98;5.66] | <0.001 | |
| D_more6_segments | 3 (0.24%) | 92 (40.5%) | 20.6 [12.3;34.7] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| A_No_ischemic_LGE | 659 (51.8%) | 44 (19.4%) | Ref. | Ref. | |
| B_Subendocardial<50% | 332 (26.1%) | 26 (11.5%) | 1.23 [0.61;2.46] | 0.409 | |
| C_Subendocardial≥50% | 257 (20.2%) | 84 (37.0%) | 4.09 [2.43;6.91] | <0.001 | |
| D_Transmural | 25 (1.96%) | 73 (32.2%) | 12.4 [7.23;21.1] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| A_No_ischemic_LGE | 659 (51.8%) | 44 (19.4%) | Ref. | Ref. | |
| B_Neither_anterior_nor_septal | 524 (41.2%) | 11 (4.85%) | 0.33 [0.13;0.85] | 0.001 | |
| C_Anterior_without_septal | 60 (4.71%) | 22 (9.69%) | 4.59 [2.21;9.56] | <0.001 | |
| D_Septal | 30 (2.36%) | 150 (66.1%) | 16.5 [10.2;26.8] | <0.001 | |
| CMR_LGE_ischemic_anterior | 72 (5.66%) | 150 (66.1%) | 14.2 [9.56;21.2] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 30 (2.36%) | 150 (66.1%) | 17.7 [11.9;26.3] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 257 (20.2%) | 27 (11.9%) | 0.59 [0.33;1.05] | 0.010 | 0.009 |
| CMR_LGE_ischemic_lateral | 189 (14.8%) | 72 (31.7%) | 2.29 [1.53;3.42] | <0.001 | <0.001 |
| CMR_LGE_ischemic_Apical | 182 (14.3%) | 100 (44.1%) | 3.80 [2.60;5.54] | <0.001 | 0.000 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 1196 (94.0%) | 124 (54.6%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 77 (6.05%) | 103 (45.4%) | 6.67 [4.58;9.72] | <0.001 | |
| CMR_LGE_presence_ischemic_and_midwall | 38 (2.99%) | 77 (33.9%) | 7.41 [4.98;11.0] | <0.001 | 0.000 |
| outcome_revascularisation_90days | 512 (40.2%) | 84 (37.0%) | 0.94 [0.64;1.39] | 0.678 | 0.679 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab17-univ-MACEpop-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab18-univ-MACEpop-MACE
df_selected <- df_MACE %>%
mutate(
surv.event = Surv(
time = time.censored,
event = outcome_MACE
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_presence_ischemic_and_midwall +
outcome_revascularisation_90days,
data= df_selected,
method = 1, conf.level = 0.995), # Pvalue
hide.no = "No", show.p.ratio = T, show.ratio = TRUE) # bug avec le hide
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=1227 | N=273 | ||||
| demo_age | 63.3 (11.8) | 68.1 (11.4) | 1.03 [1.02;1.05] | <0.001 | <0.001 |
| demo_gender | 892 (72.7%) | 231 (84.6%) | 1.95 [1.22;3.12] | <0.001 | <0.001 |
| demo_BMI | 27.7 (5.58) | 27.5 (4.95) | 0.99 [0.96;1.03] | 0.636 | 0.636 |
| CV_risk_diabete | 410 (33.4%) | 137 (50.2%) | 1.91 [1.36;2.68] | <0.001 | <0.001 |
| CV_risk_HTA | 514 (41.9%) | 136 (49.8%) | 1.34 [0.95;1.89] | 0.015 | 0.015 |
| CV_risk_obesity | 336 (27.4%) | 71 (26.0%) | 0.96 [0.65;1.42] | 0.790 | 0.789 |
| CV_risk_dyslipidemia | 580 (47.3%) | 128 (46.9%) | 1.07 [0.76;1.51] | 0.555 | 0.553 |
| CV_risk_Smoking | 215 (17.5%) | 50 (18.3%) | 1.08 [0.69;1.67] | 0.635 | 0.631 |
| history_med_MI | 473 (38.5%) | 100 (36.6%) | 0.96 [0.67;1.37] | 0.741 | 0.741 |
| history_coronary_procedure | 1087 (88.6%) | 241 (88.3%) | 1.00 [0.59;1.69] | 0.980 | 0.980 |
| history_interv_PCI | 79 (6.44%) | 26 (9.52%) | 1.42 [0.78;2.59] | 0.101 | 0.100 |
| history_interv_CABG | 1024 (83.5%) | 216 (79.1%) | 0.80 [0.53;1.23] | 0.149 | 0.148 |
| med_periph_atheroma | 58 (4.73%) | 28 (10.3%) | 2.22 [1.27;3.89] | <0.001 | <0.001 |
| history_stroke | 35 (2.85%) | 15 (5.49%) | 1.69 [0.79;3.62] | 0.055 | 0.046 |
| med_pacemaker | 13 (1.06%) | 3 (1.10%) | 1.04 [0.20;5.31] | 0.947 | 0.946 |
| med_CKD | 23 (1.87%) | 11 (4.03%) | 2.50 [1.05;5.93] | 0.003 | 0.002 |
| history_hospit_HF | 71 (5.79%) | 24 (8.79%) | 1.34 [0.74;2.45] | 0.168 | 0.168 |
| history_AFib | 87 (7.09%) | 41 (15.0%) | 2.27 [1.41;3.65] | <0.001 | <0.001 |
| clini_NYHA | 162 (13.2%) | 36 (13.2%) | 0.89 [0.54;1.47] | 0.517 | 0.516 |
| clini_cardiac_rythm | 86 (7.01%) | 38 (13.9%) | 2.16 [1.32;3.53] | <0.001 | <0.001 |
| CMR_LVEF_5 | 8.78 (1.16) | 8.53 (1.18) | 0.86 [0.75;0.98] | 0.001 | 0.001 |
| CMR_LVEDV_5 | 10.3 (2.17) | 10.6 (2.38) | 1.06 [0.98;1.14] | 0.027 | 0.027 |
| CMR_LVESV_5 | 5.83 (1.81) | 6.16 (1.92) | 1.08 [1.00;1.18] | 0.006 | 0.006 |
| CMR_LV_mass | 89.8 (20.2) | 92.2 (20.1) | 1.01 [1.00;1.02] | 0.020 | 0.020 |
| CMR_RV_dysfunction | 28 (2.28%) | 32 (11.7%) | 3.94 [2.32;6.69] | <0.001 | <0.001 |
| CMR_LGE_ischemic_presence: | <0.001 | ||||
| No_ischemic_LGE | 634 (51.7%) | 69 (25.3%) | Ref. | Ref. | |
| Presence_of_ischemic_LGE | 593 (48.3%) | 204 (74.7%) | 2.73 [1.85;4.03] | <0.001 | |
| CMR_LGE_ischemic_extent_count | 1.31 (1.64) | 3.86 (2.66) | 1.50 [1.40;1.61] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| A_No_ischemic_LGE | 634 (51.7%) | 69 (25.3%) | Ref. | Ref. | |
| B_1_2_segments | 275 (22.4%) | 9 (3.30%) | 0.31 [0.11;0.84] | 0.001 | |
| C_3_5_segments | 307 (25.0%) | 111 (40.7%) | 2.88 [1.87;4.43] | <0.001 | |
| D_more6_segments | 11 (0.90%) | 84 (30.8%) | 12.1 [7.65;19.1] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| A_No_ischemic_LGE | 634 (51.7%) | 69 (25.3%) | Ref. | Ref. | |
| B_Subendocardial<50% | 319 (26.0%) | 39 (14.3%) | 1.16 [0.66;2.04] | 0.448 | |
| C_Subendocardial≥50% | 248 (20.2%) | 93 (34.1%) | 2.90 [1.85;4.53] | <0.001 | |
| D_Transmural | 26 (2.12%) | 72 (26.4%) | 7.86 [4.89;12.6] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| A_No_ischemic_LGE | 634 (51.7%) | 69 (25.3%) | Ref. | Ref. | |
| B_Neither_anterior_nor_septal | 503 (41.0%) | 32 (11.7%) | 0.61 [0.33;1.10] | 0.019 | |
| C_Anterior_without_septal | 63 (5.13%) | 19 (6.96%) | 2.50 [1.21;5.18] | <0.001 | |
| D_Septal | 27 (2.20%) | 153 (56.0%) | 10.9 [7.23;16.4] | <0.001 | |
| CMR_LGE_ischemic_anterior | 72 (5.87%) | 150 (54.9%) | 8.89 [6.30;12.5] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 27 (2.20%) | 153 (56.0%) | 11.7 [8.32;16.6] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 234 (19.1%) | 50 (18.3%) | 0.99 [0.64;1.54] | 0.953 | 0.954 |
| CMR_LGE_ischemic_lateral | 173 (14.1%) | 88 (32.2%) | 2.38 [1.65;3.43] | <0.001 | <0.001 |
| CMR_LGE_ischemic_Apical | 179 (14.6%) | 103 (37.7%) | 2.89 [2.04;4.11] | <0.001 | 0.000 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 1141 (93.0%) | 179 (65.6%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 86 (7.01%) | 94 (34.4%) | 4.28 [2.99;6.14] | <0.001 | |
| CMR_LGE_presence_ischemic_and_midwall | 43 (3.50%) | 72 (26.4%) | 5.33 [3.62;7.86] | <0.001 | 0.000 |
| outcome_revascularisation_90days | 490 (39.9%) | 106 (38.8%) | 1.01 [0.71;1.43] | 0.934 | 0.933 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab18-univ-MACEpop-MACE-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab19-multi_tradi-MACEpop-MACE
df <- df_MACE %>% mutate(
event = outcome_MACE,
time = time.censored
) %>% select(
event, time,
demo_age, demo_gender, demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF, history_AFib, med_CKD, history_med_MI , CMR_LVEF, CMR_LGE_ischemic_presence) %>% droplevels()
#### TABLE
## Model 1
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LVEF))
tbl_regression(model, exponentiate = FALSE, conf.level = 0.995, pvalue_fun = function(x) style_pvalue(x, digits = 3), label = list()) %>% bold_p() %>%
as_gt() %>% gt::tab_header(gt::md("score coef - model 1"))| score coef - model 1 | |||
| Characteristic | log(HR)1 | 100% CI1 | p-value |
|---|---|---|---|
| demo_age | 0.03 | 0.01, 0.05 | <0.001 |
| demo_gender | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.67 | 0.19, 1.2 | <0.001 |
| demo_BMI | -0.01 | -0.05, 0.02 | 0.326 |
| CV_risk_diabete | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.56 | 0.20, 0.93 | <0.001 |
| CV_risk_Smoking | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.23 | -0.23, 0.68 | 0.163 |
| CV_risk_dyslipidemia | |||
|     No | — | — | |
| Â Â Â Â Yes | -0.03 | -0.38, 0.32 | 0.835 |
| history_hospit_HF | |||
|     No | — | — | |
| Â Â Â Â Yes | -0.03 | -0.65, 0.60 | 0.896 |
| history_AFib | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.42 | -0.08, 0.92 | 0.020 |
| med_CKD | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.29 | -0.60, 1.2 | 0.366 |
| history_med_MI | |||
|     No | — | — | |
| Â Â Â Â Yes | -0.09 | -0.46, 0.27 | 0.475 |
| CMR_LVEF | -0.02 | -0.04, 0.01 | 0.085 |
| 1 HR = Hazard Ratio, CI = Confidence Interval | |||
## Model 2
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_presence))
tbl_regression(model, exponentiate = FALSE, conf.level = 0.995, pvalue_fun = function(x) style_pvalue(x, digits = 3), label = list()) %>% bold_p() %>%
as_gt() %>% gt::tab_header(gt::md("score coef - model 2"))| score coef - model 2 | |||
| Characteristic | log(HR)1 | 100% CI1 | p-value |
|---|---|---|---|
| demo_age | 0.03 | 0.01, 0.05 | <0.001 |
| demo_gender | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.63 | 0.15, 1.1 | <0.001 |
| demo_BMI | -0.01 | -0.05, 0.02 | 0.351 |
| CV_risk_diabete | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.48 | 0.11, 0.84 | <0.001 |
| CV_risk_Smoking | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.25 | -0.21, 0.71 | 0.129 |
| CV_risk_dyslipidemia | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.00 | -0.35, 0.35 | 0.992 |
| history_hospit_HF | |||
|     No | — | — | |
| Â Â Â Â Yes | -0.03 | -0.65, 0.60 | 0.911 |
| history_AFib | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.40 | -0.11, 0.90 | 0.028 |
| med_CKD | |||
|     No | — | — | |
| Â Â Â Â Yes | 0.25 | -0.65, 1.1 | 0.438 |
| history_med_MI | |||
|     No | — | — | |
| Â Â Â Â Yes | -0.08 | -0.44, 0.29 | 0.560 |
| CMR_LVEF | -0.01 | -0.04, 0.02 | 0.348 |
| CMR_LGE_ischemic_presence | |||
|     No_ischemic_LGE | — | — | |
| Â Â Â Â Presence_of_ischemic_LGE | 0.91 | 0.51, 1.3 | <0.001 |
| 1 HR = Hazard Ratio, CI = Confidence Interval | |||
# Table uni multi
table <- uni_multi_function(
df = df,
event = "event",
time = "time",
var_base = c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF"),
var_added = c("CMR_LGE_ischemic_presence"))
# export in html
knitr::kable(table, caption = "Model X - LGE presence - MACE population (N=1,500)", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Variable | Univariate_Analysis | model_multi_1 | model_multi_2 |
|---|---|---|---|
| demo_age | 1.03 (1.02-1.05), p=0.000 | 1.03 (1.01-1.05), p=0.000 | 1.03 (1.01-1.05), p=0.000 |
| demo_genderYes | 1.95 (1.22-3.12), p=0.000 | 1.96 (1.21-3.17), p=0.000 | 1.87 (1.16-3.03), p=0.000 |
| demo_BMI | 0.99 (0.96-1.03), p=0.636 | 0.99 (0.95-1.02), p=0.326 | 0.99 (0.95-1.02), p=0.351 |
| CV_risk_diabeteYes | 1.91 (1.36-2.68), p=0.000 | 1.76 (1.22-2.53), p=0.000 | 1.61 (1.12-2.33), p=0.000 |
| CV_risk_SmokingYes | 1.08 (0.69-1.67), p=0.635 | 1.25 (0.80-1.98), p=0.163 | 1.28 (0.81-2.03), p=0.129 |
| CV_risk_dyslipidemiaYes | 1.07 (0.76-1.51), p=0.555 | 0.97 (0.69-1.38), p=0.835 | 1.00 (0.71-1.42), p=0.992 |
| history_hospit_HFYes | 1.34 (0.74-2.45), p=0.168 | 0.97 (0.52-1.82), p=0.896 | 0.98 (0.52-1.83), p=0.911 |
| history_AFibYes | 2.27 (1.41-3.65), p=0.000 | 1.52 (0.92-2.51), p=0.020 | 1.49 (0.90-2.46), p=0.028 |
| med_CKDYes | 2.50 (1.05-5.93), p=0.003 | 1.33 (0.55-3.25), p=0.366 | 1.28 (0.52-3.12), p=0.438 |
| history_med_MIYes | 0.96 (0.67-1.37), p=0.741 | 0.91 (0.63-1.31), p=0.475 | 0.93 (0.64-1.33), p=0.560 |
| CMR_LVEF | 0.97 (0.95-1.00), p=0.001 | 0.98 (0.96-1.01), p=0.085 | 0.99 (0.96-1.02), p=0.348 |
| CMR_LGE_ischemic_presencePresence_of_ischemic_LGE | 2.73 (1.85-4.03), p=0.000 | NA | 2.47 (1.67-3.68), p=0.000 |
# Export in word
ft <- flextable(table)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Model X - LGE presence in all pop (N=6,082)", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab19-multi_tradi-MACEpop-MACE-",Sys.Date(), ".docx")))MACE population + LGE (N=797)
Survival
Tab20-univ-MACE_LGEpop-death
df_selected <- df_MACE %>%
filter(CMR_LGE_ischemic_presence == "Presence_of_ischemic_LGE") %>%
mutate(
surv.event = Surv(
time = outcome_FU_time_death,
event = outcome_death
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_presence_ischemic_and_midwall +
outcome_revascularisation_90days,
data= df_selected,
method = 1, conf.level = 0.995), # Pvalue
hide.no = "No", show.p.ratio = T, show.ratio = TRUE) # bug avec le hide
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=614 | N=183 | ||||
| demo_age | 62.9 (11.3) | 68.9 (11.1) | 1.04 [1.02;1.06] | <0.001 | <0.001 |
| demo_gender | 458 (74.6%) | 159 (86.9%) | 2.11 [1.14;3.90] | 0.001 | <0.001 |
| demo_BMI | 27.9 (6.13) | 27.7 (5.14) | 1.00 [0.96;1.03] | 0.786 | 0.786 |
| CV_risk_diabete | 222 (36.2%) | 109 (59.6%) | 2.28 [1.49;3.48] | <0.001 | <0.001 |
| CV_risk_HTA | 262 (42.7%) | 95 (51.9%) | 1.46 [0.96;2.22] | 0.011 | 0.010 |
| CV_risk_obesity | 167 (27.2%) | 53 (29.0%) | 1.08 [0.68;1.71] | 0.636 | 0.632 |
| CV_risk_dyslipidemia | 277 (45.1%) | 96 (52.5%) | 1.41 [0.93;2.15] | 0.020 | 0.019 |
| CV_risk_Smoking | 111 (18.1%) | 33 (18.0%) | 1.01 [0.59;1.73] | 0.966 | 0.959 |
| history_med_MI | 246 (40.1%) | 63 (34.4%) | 0.86 [0.55;1.33] | 0.335 | 0.334 |
| history_coronary_procedure | 540 (87.9%) | 162 (88.5%) | 1.03 [0.54;1.98] | 0.886 | 0.882 |
| history_interv_PCI | 40 (6.51%) | 20 (10.9%) | 1.66 [0.84;3.28] | 0.038 | 0.036 |
| history_interv_CABG | 506 (82.4%) | 142 (77.6%) | 0.76 [0.46;1.25] | 0.123 | 0.123 |
| med_periph_atheroma | 26 (4.23%) | 24 (13.1%) | 3.04 [1.64;5.66] | <0.001 | <0.001 |
| history_stroke | 22 (3.58%) | 12 (6.56%) | 1.76 [0.76;4.08] | 0.058 | 0.055 |
| med_pacemaker | 8 (1.30%) | 0 (0.00%) | 0.00 [0.00;.] | 0.994 | 0.137 |
| med_CKD | 11 (1.79%) | 9 (4.92%) | 2.48 [0.95;6.49] | 0.008 | 0.006 |
| history_hospit_HF | 35 (5.70%) | 21 (11.5%) | 1.67 [0.87;3.21] | 0.027 | 0.025 |
| history_AFib | 42 (6.84%) | 27 (14.8%) | 1.91 [1.07;3.44] | 0.002 | 0.002 |
| clini_NYHA | 89 (14.5%) | 23 (12.6%) | 0.74 [0.39;1.38] | 0.176 | 0.174 |
| clini_cardiac_rythm | 42 (6.84%) | 26 (14.2%) | 1.84 [1.01;3.33] | 0.004 | 0.004 |
| CMR_LVEF_5 | 8.72 (1.22) | 8.40 (1.26) | 0.87 [0.75;1.01] | 0.010 | 0.010 |
| CMR_LVEDV_5 | 10.4 (2.37) | 10.7 (2.50) | 1.03 [0.95;1.12] | 0.323 | 0.323 |
| CMR_LVESV_5 | 5.95 (1.97) | 6.27 (2.02) | 1.06 [0.96;1.17] | 0.104 | 0.104 |
| CMR_LV_mass | 89.5 (20.9) | 92.3 (19.4) | 1.01 [1.00;1.02] | 0.010 | 0.010 |
| CMR_RV_dysfunction | 20 (3.26%) | 25 (13.7%) | 3.51 [1.91;6.45] | <0.001 | <0.001 |
| CMR_LGE_ischemic_presence: Presence_of_ischemic_LGE | 614 (100%) | 183 (100%) | Ref. | Ref. | . |
| CMR_LGE_ischemic_extent_count | 2.74 (1.29) | 5.33 (1.75) | 1.78 [1.60;1.97] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| B_1_2_segments | 276 (45.0%) | 8 (4.37%) | Ref. | Ref. | |
| C_3_5_segments | 335 (54.6%) | 83 (45.4%) | 7.64 [2.70;21.6] | <0.001 | |
| D_more6_segments | 3 (0.49%) | 92 (50.3%) | 46.5 [16.5;131] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| B_Subendocardial<50% | 332 (54.1%) | 26 (14.2%) | Ref. | Ref. | |
| C_Subendocardial≥50% | 257 (41.9%) | 84 (45.9%) | 3.37 [1.79;6.33] | <0.001 | |
| D_Transmural | 25 (4.07%) | 73 (39.9%) | 10.1 [5.29;19.1] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| B_Neither_anterior_nor_septal | 524 (85.3%) | 11 (6.01%) | Ref. | Ref. | |
| C_Anterior_without_septal | 60 (9.77%) | 22 (12.0%) | 14.1 [4.98;39.6] | <0.001 | |
| D_Septal | 30 (4.89%) | 150 (82.0%) | 50.4 [21.0;121] | <0.001 | |
| CMR_LGE_ischemic_anterior | 72 (11.7%) | 150 (82.0%) | 15.1 [8.72;26.1] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 30 (4.89%) | 150 (82.0%) | 19.2 [11.2;33.0] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 257 (41.9%) | 27 (14.8%) | 0.31 [0.17;0.55] | <0.001 | <0.001 |
| CMR_LGE_ischemic_lateral | 189 (30.8%) | 72 (39.3%) | 1.35 [0.88;2.08] | 0.046 | 0.045 |
| CMR_LGE_ischemic_Apical | 182 (29.6%) | 100 (54.6%) | 2.43 [1.60;3.69] | <0.001 | <0.001 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 576 (93.8%) | 106 (57.9%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 38 (6.19%) | 77 (42.1%) | 5.17 [3.39;7.89] | <0.001 | |
| CMR_LGE_presence_ischemic_and_midwall | 38 (6.19%) | 77 (42.1%) | 5.17 [3.39;7.89] | <0.001 | 0.000 |
| outcome_revascularisation_90days | 512 (83.4%) | 84 (45.9%) | 0.30 [0.19;0.45] | <0.001 | 0.000 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab20-univ-MACE_LGEpop-death-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab21-univ-MACE_LGEpop-MACE
df_selected <- df_MACE %>%
filter(CMR_LGE_ischemic_presence == "Presence_of_ischemic_LGE") %>%
mutate(
surv.event = Surv(
time = time.censored,
event = outcome_MACE
),
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LVEDV_5 = CMR_LVEDV/10,
CMR_LVESV_5 = CMR_LVESV/10
)
coxph(surv.event ~ med_pacemaker, data = df_selected)## Call:
## coxph(formula = surv.event ~ med_pacemaker, data = df_selected)
##
## coef exp(coef) se(coef) z p
## med_pacemakerYes -1.502e+01 3.003e-07 1.235e+03 -0.012 0.99
##
## Likelihood ratio test=4.34 on 1 df, p=0.03728
## n= 797, number of events= 204Descr_table = createTable(compareGroups(
surv.event ~
demo_age + demo_gender + demo_BMI + CV_risk_diabete +
CV_risk_HTA + CV_risk_obesity + CV_risk_dyslipidemia + CV_risk_Smoking +
history_med_MI + history_coronary_procedure + history_interv_PCI +
history_interv_CABG + med_periph_atheroma + history_stroke +
med_pacemaker + med_CKD + history_hospit_HF + history_AFib + clini_NYHA +
clini_cardiac_rythm +
CMR_LVEF_5 + CMR_LVEDV_5 +
CMR_LVESV_5 + CMR_LV_mass + CMR_RV_dysfunction +
CMR_LGE_ischemic_presence +
CMR_LGE_ischemic_extent_count +
CMR_LGE_ischemic_extent_categ +
CMR_LGE_ischemic_transmurality +
CMR_LGE_ischemic_location_4 +
CMR_LGE_ischemic_anterior +
CMR_LGE_ischemic_septal +
CMR_LGE_ischemic_inferior +
CMR_LGE_ischemic_lateral +
CMR_LGE_ischemic_Apical +
CMR_LGE_midwall_presence +
CMR_LGE_presence_ischemic_and_midwall +
outcome_revascularisation_90days,
data= df_selected,
method = 1, conf.level = 0.995), # Pvalue
hide.no = "No", show.p.ratio = T, show.ratio = TRUE) # bug avec le hide
export2md(Descr_table, strip=TRUE, first.strip=TRUE)| No event | Event | HR | p.ratio | p.overall | |
|---|---|---|---|---|---|
| N=593 | N=204 | ||||
| demo_age | 62.7 (11.3) | 68.9 (11.0) | 1.04 [1.02;1.06] | <0.001 | <0.001 |
| demo_gender | 446 (75.2%) | 171 (83.8%) | 1.63 [0.96;2.78] | 0.010 | 0.009 |
| demo_BMI | 28.0 (6.20) | 27.5 (4.99) | 0.99 [0.96;1.03] | 0.496 | 0.496 |
| CV_risk_diabete | 223 (37.6%) | 108 (52.9%) | 1.74 [1.18;2.59] | <0.001 | <0.001 |
| CV_risk_HTA | 251 (42.3%) | 106 (52.0%) | 1.47 [0.99;2.18] | 0.006 | 0.006 |
| CV_risk_obesity | 167 (28.2%) | 53 (26.0%) | 0.93 [0.59;1.46] | 0.662 | 0.663 |
| CV_risk_dyslipidemia | 271 (45.7%) | 102 (50.0%) | 1.27 [0.85;1.88] | 0.092 | 0.091 |
| CV_risk_Smoking | 109 (18.4%) | 35 (17.2%) | 0.96 [0.57;1.62] | 0.843 | 0.849 |
| history_med_MI | 236 (39.8%) | 73 (35.8%) | 0.91 [0.60;1.37] | 0.517 | 0.518 |
| history_coronary_procedure | 523 (88.2%) | 179 (87.7%) | 0.96 [0.53;1.75] | 0.860 | 0.860 |
| history_interv_PCI | 41 (6.91%) | 19 (9.31%) | 1.35 [0.67;2.71] | 0.223 | 0.222 |
| history_interv_CABG | 488 (82.3%) | 160 (78.4%) | 0.80 [0.50;1.30] | 0.205 | 0.204 |
| med_periph_atheroma | 27 (4.55%) | 23 (11.3%) | 2.47 [1.32;4.62] | <0.001 | <0.001 |
| history_stroke | 21 (3.54%) | 13 (6.37%) | 1.70 [0.76;3.81] | 0.064 | 0.061 |
| med_pacemaker | 8 (1.35%) | 0 (0.00%) | 0.00 [0.00;.] | 0.990 | 0.139 |
| med_CKD | 12 (2.02%) | 8 (3.92%) | 1.95 [0.71;5.38] | 0.064 | 0.060 |
| history_hospit_HF | 35 (5.90%) | 21 (10.3%) | 1.51 [0.79;2.88] | 0.075 | 0.074 |
| history_AFib | 41 (6.91%) | 28 (13.7%) | 1.80 [1.02;3.20] | 0.004 | 0.003 |
| clini_NYHA | 86 (14.5%) | 26 (12.7%) | 0.75 [0.42;1.36] | 0.176 | 0.174 |
| clini_cardiac_rythm | 40 (6.75%) | 28 (13.7%) | 1.81 [1.02;3.21] | 0.003 | 0.003 |
| CMR_LVEF_5 | 8.71 (1.24) | 8.47 (1.22) | 0.91 [0.78;1.05] | 0.060 | 0.060 |
| CMR_LVEDV_5 | 10.4 (2.39) | 10.7 (2.42) | 1.03 [0.95;1.12] | 0.278 | 0.278 |
| CMR_LVESV_5 | 5.95 (2.00) | 6.23 (1.95) | 1.05 [0.95;1.15] | 0.161 | 0.161 |
| CMR_LV_mass | 89.2 (20.7) | 93.0 (20.2) | 1.01 [1.00;1.02] | 0.002 | 0.002 |
| CMR_RV_dysfunction | 17 (2.87%) | 28 (13.7%) | 3.72 [2.09;6.62] | <0.001 | <0.001 |
| CMR_LGE_ischemic_presence: Presence_of_ischemic_LGE | 593 (100%) | 204 (100%) | Ref. | Ref. | . |
| CMR_LGE_ischemic_extent_count | 2.70 (1.33) | 5.17 (1.65) | 1.71 [1.56;1.88] | <0.001 | <0.001 |
| CMR_LGE_ischemic_extent_categ: | 0.000 | ||||
| B_1_2_segments | 275 (46.4%) | 9 (4.41%) | Ref. | Ref. | |
| C_3_5_segments | 307 (51.8%) | 111 (54.4%) | 9.33 [3.52;24.7] | <0.001 | |
| D_more6_segments | 11 (1.85%) | 84 (41.2%) | 39.0 [14.6;104] | <0.001 | |
| CMR_LGE_ischemic_transmurality: | 0.000 | ||||
| B_Subendocardial<50% | 319 (53.8%) | 39 (19.1%) | Ref. | Ref. | |
| C_Subendocardial≥50% | 248 (41.8%) | 93 (45.6%) | 2.50 [1.46;4.28] | <0.001 | |
| D_Transmural | 26 (4.38%) | 72 (35.3%) | 6.74 [3.85;11.8] | <0.001 | |
| CMR_LGE_ischemic_location_4: | 0.000 | ||||
| B_Neither_anterior_nor_septal | 503 (84.8%) | 32 (15.7%) | Ref. | Ref. | |
| C_Anterior_without_septal | 63 (10.6%) | 19 (9.31%) | 4.14 [1.84;9.35] | <0.001 | |
| D_Septal | 27 (4.55%) | 153 (75.0%) | 18.0 [10.4;31.2] | <0.001 | |
| CMR_LGE_ischemic_anterior | 72 (12.1%) | 150 (73.5%) | 9.21 [5.88;14.4] | <0.001 | 0.000 |
| CMR_LGE_ischemic_septal | 27 (4.55%) | 153 (75.0%) | 12.9 [8.21;20.4] | <0.001 | 0.000 |
| CMR_LGE_ischemic_inferior | 234 (39.5%) | 50 (24.5%) | 0.59 [0.37;0.93] | 0.001 | 0.001 |
| CMR_LGE_ischemic_lateral | 173 (29.2%) | 88 (43.1%) | 1.62 [1.09;2.41] | 0.001 | 0.001 |
| CMR_LGE_ischemic_Apical | 179 (30.2%) | 103 (50.5%) | 2.04 [1.37;3.02] | <0.001 | <0.001 |
| CMR_LGE_midwall_presence: | 0.000 | ||||
| A_No_midwall_LGE | 550 (92.7%) | 132 (64.7%) | Ref. | Ref. | |
| B_Presence_of_midwall_LGE | 43 (7.25%) | 72 (35.3%) | 4.01 [2.65;6.07] | <0.001 | |
| CMR_LGE_presence_ischemic_and_midwall | 43 (7.25%) | 72 (35.3%) | 4.01 [2.65;6.07] | <0.001 | 0.000 |
| outcome_revascularisation_90days | 490 (82.6%) | 106 (52.0%) | 0.37 [0.25;0.55] | <0.001 | <0.001 |
export2word(x = Descr_table,
file = here(tables_output_dir, paste0("Tab21-univ-MACE_LGEpop-MACE-",Sys.Date(), ".docx")),
which.table="descr", nmax=TRUE, header.labels=c(),
caption=NULL, strip=FALSE, first.strip=FALSE, background="#D2D2D2",
size=NULL, header.background=NULL, header.color=NULL)Tab22-multi_tradi-MACE_LGEpop-MACE
df <- df_MACE %>% filter(CMR_LGE_ischemic_presence == "Presence_of_ischemic_LGE") %>%
mutate(
event = outcome_MACE,
time = time.censored,
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LGE_ischemic_transmurality_bin = case_when(
CMR_LGE_ischemic_transmurality == "B_Subendocardial<50%" ~ "A_viable",
CMR_LGE_ischemic_transmurality != "B_Subendocardial<50%" ~ "B_non_viable",
TRUE ~ NA
)
) %>% select(
event, time,
demo_age, demo_gender, demo_BMI , CV_risk_diabete , CV_risk_Smoking , CV_risk_dyslipidemia , history_hospit_HF, history_AFib, med_CKD, history_med_MI , CMR_LVEF_5, CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_transmurality_bin, CMR_LGE_ischemic_location_4, CMR_LGE_midwall_presence, outcome_revascularisation_90days) %>% droplevels()
#### TABLE
## Model 1
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LVEF_5))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 1", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.04 ( 0.02 - 0.06 ) | 1.04 ( 1.02 - 1.06 ) | <0.001 |
| demo_genderYes | 0.51 ( -0.03 - 1.06 ) | 1.67 ( 0.97 - 2.89 ) | 0.008 |
| demo_BMI | -0.01 ( -0.05 - 0.03 ) | 0.99 ( 0.95 - 1.03 ) | 0.412 |
| CV_risk_diabeteYes | 0.47 ( 0.04 - 0.89 ) | 1.59 ( 1.04 - 2.43 ) | 0.002 |
| CV_risk_SmokingYes | 0.24 ( -0.31 - 0.79 ) | 1.27 ( 0.73 - 2.20 ) | 0.221 |
| CV_risk_dyslipidemiaYes | 0.16 ( -0.25 - 0.56 ) | 1.17 ( 0.78 - 1.75 ) | 0.277 |
| history_hospit_HFYes | 0.12 ( -0.56 - 0.80 ) | 1.13 ( 0.57 - 2.23 ) | 0.623 |
| history_AFibYes | 0.13 ( -0.48 - 0.73 ) | 1.13 ( 0.62 - 2.08 ) | 0.563 |
| med_CKDYes | 0.15 ( -0.90 - 1.19 ) | 1.16 ( 0.41 - 3.30 ) | 0.691 |
| history_med_MIYes | -0.09 ( -0.51 - 0.34 ) | 0.92 ( 0.60 - 1.41 ) | 0.574 |
| CMR_LVEF_5 | -0.03 ( -0.18 - 0.13 ) | 0.97 ( 0.83 - 1.14 ) | 0.646 |
write.csv(data, file = here("outputs", "csv_files","MACE_pop_model1_trad.csv"), append = FALSE)
## Model 2
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 2", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( -0.00 - 0.04 ) | 1.02 ( 1.00 - 1.04 ) | 0.005 |
| demo_genderYes | 0.30 ( -0.25 - 0.86 ) | 1.35 ( 0.78 - 2.35 ) | 0.127 |
| demo_BMI | -0.01 ( -0.05 - 0.04 ) | 0.99 ( 0.95 - 1.04 ) | 0.664 |
| CV_risk_diabeteYes | 0.25 ( -0.17 - 0.67 ) | 1.28 ( 0.84 - 1.95 ) | 0.096 |
| CV_risk_SmokingYes | 0.22 ( -0.34 - 0.78 ) | 1.24 ( 0.71 - 2.17 ) | 0.280 |
| CV_risk_dyslipidemiaYes | 0.14 ( -0.28 - 0.55 ) | 1.14 ( 0.75 - 1.74 ) | 0.366 |
| history_hospit_HFYes | 0.01 ( -0.71 - 0.73 ) | 1.01 ( 0.49 - 2.08 ) | 0.969 |
| history_AFibYes | -0.04 ( -0.68 - 0.60 ) | 0.96 ( 0.51 - 1.83 ) | 0.864 |
| med_CKDYes | 0.42 ( -0.69 - 1.53 ) | 1.52 ( 0.50 - 4.60 ) | 0.290 |
| history_med_MIYes | 0.23 ( -0.21 - 0.67 ) | 1.26 ( 0.81 - 1.96 ) | 0.147 |
| CMR_LVEF_5 | 0.02 ( -0.15 - 0.20 ) | 1.02 ( 0.86 - 1.22 ) | 0.700 |
| CMR_LGE_ischemic_extent_count | 0.50 ( 0.40 - 0.61 ) | 1.66 ( 1.49 - 1.83 ) | <0.001 |
## Model 3A
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_transmurality_bin))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3A", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( -0.00 - 0.04 ) | 1.02 ( 1.00 - 1.04 ) | 0.014 |
| demo_genderYes | 0.30 ( -0.26 - 0.86 ) | 1.35 ( 0.77 - 2.35 ) | 0.130 |
| demo_BMI | -0.01 ( -0.05 - 0.03 ) | 0.99 ( 0.95 - 1.03 ) | 0.566 |
| CV_risk_diabeteYes | 0.24 ( -0.18 - 0.66 ) | 1.27 ( 0.83 - 1.94 ) | 0.110 |
| CV_risk_SmokingYes | 0.18 ( -0.38 - 0.73 ) | 1.19 ( 0.68 - 2.09 ) | 0.372 |
| CV_risk_dyslipidemiaYes | 0.16 ( -0.26 - 0.58 ) | 1.17 ( 0.77 - 1.78 ) | 0.297 |
| history_hospit_HFYes | -0.06 ( -0.78 - 0.67 ) | 0.95 ( 0.46 - 1.95 ) | 0.829 |
| history_AFibYes | -0.09 ( -0.73 - 0.56 ) | 0.92 ( 0.48 - 1.75 ) | 0.709 |
| med_CKDYes | 0.40 ( -0.71 - 1.50 ) | 1.49 ( 0.49 - 4.50 ) | 0.316 |
| history_med_MIYes | 0.31 ( -0.14 - 0.76 ) | 1.37 ( 0.87 - 2.14 ) | 0.051 |
| CMR_LVEF_5 | 0.03 ( -0.15 - 0.20 ) | 1.03 ( 0.86 - 1.23 ) | 0.661 |
| CMR_LGE_ischemic_extent_count | 0.48 ( 0.37 - 0.58 ) | 1.61 ( 1.45 - 1.79 ) | <0.001 |
| CMR_LGE_ischemic_transmurality_binB_non_viable | 0.79 ( 0.27 - 1.31 ) | 2.21 ( 1.32 - 3.71 ) | <0.001 |
## Model 3B
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_ischemic_location_4))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3B", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( -0.00 - 0.04 ) | 1.02 ( 1.00 - 1.04 ) | 0.012 |
| demo_genderYes | 0.12 ( -0.45 - 0.68 ) | 1.13 ( 0.64 - 1.98 ) | 0.553 |
| demo_BMI | -0.00 ( -0.05 - 0.04 ) | 1.00 ( 0.96 - 1.04 ) | 0.909 |
| CV_risk_diabeteYes | 0.16 ( -0.26 - 0.58 ) | 1.18 ( 0.77 - 1.79 ) | 0.273 |
| CV_risk_SmokingYes | 0.20 ( -0.37 - 0.76 ) | 1.22 ( 0.69 - 2.14 ) | 0.332 |
| CV_risk_dyslipidemiaYes | 0.17 ( -0.25 - 0.60 ) | 1.19 ( 0.78 - 1.82 ) | 0.259 |
| history_hospit_HFYes | 0.06 ( -0.68 - 0.80 ) | 1.06 ( 0.51 - 2.23 ) | 0.814 |
| history_AFibYes | -0.03 ( -0.67 - 0.62 ) | 0.97 ( 0.51 - 1.86 ) | 0.908 |
| med_CKDYes | -0.08 ( -1.21 - 1.04 ) | 0.92 ( 0.30 - 2.82 ) | 0.833 |
| history_med_MIYes | 0.21 ( -0.23 - 0.66 ) | 1.24 ( 0.79 - 1.93 ) | 0.178 |
| CMR_LVEF_5 | 0.12 ( -0.07 - 0.31 ) | 1.13 ( 0.93 - 1.36 ) | 0.077 |
| CMR_LGE_ischemic_extent_count | 0.22 ( 0.10 - 0.34 ) | 1.25 ( 1.10 - 1.41 ) | <0.001 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.15 ( 0.31 - 1.99 ) | 3.17 ( 1.37 - 7.32 ) | <0.001 |
| CMR_LGE_ischemic_location_4D_Septal | 2.29 ( 1.65 - 2.93 ) | 9.88 ( 5.19 - 18.80 ) | <0.001 |
## Model 3C
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event:CMR_LGE_ischemic_extent_count, CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 3C", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( -0.01 - 0.04 ) | 1.02 ( 0.99 - 1.04 ) | 0.042 |
| demo_genderYes | 0.42 ( -0.14 - 0.99 ) | 1.53 ( 0.87 - 2.69 ) | 0.034 |
| demo_BMI | -0.01 ( -0.06 - 0.03 ) | 0.99 ( 0.95 - 1.03 ) | 0.395 |
| CV_risk_diabeteYes | 0.28 ( -0.14 - 0.70 ) | 1.33 ( 0.87 - 2.02 ) | 0.057 |
| CV_risk_SmokingYes | 0.21 ( -0.35 - 0.77 ) | 1.24 ( 0.70 - 2.17 ) | 0.289 |
| CV_risk_dyslipidemiaYes | 0.16 ( -0.26 - 0.57 ) | 1.17 ( 0.77 - 1.78 ) | 0.285 |
| history_hospit_HFYes | 0.05 ( -0.67 - 0.77 ) | 1.05 ( 0.51 - 2.16 ) | 0.841 |
| history_AFibYes | 0.01 ( -0.63 - 0.64 ) | 1.01 ( 0.53 - 1.90 ) | 0.982 |
| med_CKDYes | 0.41 ( -0.68 - 1.50 ) | 1.51 ( 0.51 - 4.48 ) | 0.290 |
| history_med_MIYes | 0.15 ( -0.30 - 0.60 ) | 1.16 ( 0.74 - 1.81 ) | 0.355 |
| CMR_LVEF_5 | 0.07 ( -0.11 - 0.25 ) | 1.07 ( 0.90 - 1.28 ) | 0.288 |
| CMR_LGE_ischemic_extent_count | 0.47 ( 0.36 - 0.57 ) | 1.60 ( 1.44 - 1.77 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.74 ( 0.29 - 1.20 ) | 2.10 ( 1.34 - 3.30 ) | <0.001 |
## Model 4
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: CMR_LGE_midwall_presence))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.01 ( -0.01 - 0.04 ) | 1.01 ( 0.99 - 1.04 ) | 0.064 |
| demo_genderYes | 0.12 ( -0.45 - 0.69 ) | 1.13 ( 0.64 - 1.99 ) | 0.558 |
| demo_BMI | -0.01 ( -0.05 - 0.03 ) | 0.99 ( 0.95 - 1.03 ) | 0.499 |
| CV_risk_diabeteYes | 0.17 ( -0.25 - 0.59 ) | 1.19 ( 0.78 - 1.81 ) | 0.246 |
| CV_risk_SmokingYes | 0.21 ( -0.35 - 0.78 ) | 1.24 ( 0.70 - 2.18 ) | 0.295 |
| CV_risk_dyslipidemiaYes | 0.27 ( -0.16 - 0.70 ) | 1.31 ( 0.85 - 2.02 ) | 0.075 |
| history_hospit_HFYes | 0.12 ( -0.62 - 0.87 ) | 1.13 ( 0.54 - 2.38 ) | 0.639 |
| history_AFibYes | 0.03 ( -0.61 - 0.67 ) | 1.03 ( 0.54 - 1.96 ) | 0.891 |
| med_CKDYes | 0.00 ( -1.11 - 1.12 ) | 1.00 ( 0.33 - 3.06 ) | 0.990 |
| history_med_MIYes | 0.16 ( -0.29 - 0.61 ) | 1.18 ( 0.75 - 1.85 ) | 0.317 |
| CMR_LVEF_5 | 0.16 ( -0.03 - 0.34 ) | 1.17 ( 0.97 - 1.41 ) | 0.020 |
| CMR_LGE_ischemic_extent_count | 0.22 ( 0.09 - 0.34 ) | 1.24 ( 1.10 - 1.40 ) | <0.001 |
| CMR_LGE_ischemic_transmurality_binB_non_viable | 0.38 ( -0.15 - 0.92 ) | 1.47 ( 0.86 - 2.51 ) | 0.045 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 0.90 ( 0.03 - 1.78 ) | 2.46 ( 1.03 - 5.92 ) | 0.004 |
| CMR_LGE_ischemic_location_4D_Septal | 2.09 ( 1.43 - 2.75 ) | 8.07 ( 4.17 - 15.62 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.65 ( 0.20 - 1.10 ) | 1.91 ( 1.22 - 3.00 ) | <0.001 |
write.csv(data, file = here("outputs", "csv_files","MACE_pop_model4_trad.csv"), append = FALSE)
## Model 5
model <- coxph(formula = Surv(time, event) ~ ., data = df %>% select(event: outcome_revascularisation_90days))
data <- extract_model_stats(model, conf_level = 0.995)
knitr::kable(data, caption = "Model 4", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | |
|---|---|---|---|
| demo_age | 0.02 ( -0.00 - 0.05 ) | 1.02 ( 1.00 - 1.05 ) | 0.007 |
| demo_genderYes | 0.17 ( -0.41 - 0.74 ) | 1.18 ( 0.67 - 2.10 ) | 0.412 |
| demo_BMI | -0.01 ( -0.06 - 0.03 ) | 0.99 ( 0.95 - 1.03 ) | 0.414 |
| CV_risk_diabeteYes | 0.19 ( -0.23 - 0.61 ) | 1.21 ( 0.80 - 1.85 ) | 0.193 |
| CV_risk_SmokingYes | 0.24 ( -0.33 - 0.81 ) | 1.27 ( 0.72 - 2.24 ) | 0.243 |
| CV_risk_dyslipidemiaYes | 0.29 ( -0.14 - 0.72 ) | 1.34 ( 0.87 - 2.05 ) | 0.057 |
| history_hospit_HFYes | 0.12 ( -0.61 - 0.86 ) | 1.13 ( 0.54 - 2.37 ) | 0.636 |
| history_AFibYes | 0.00 ( -0.64 - 0.64 ) | 1.00 ( 0.53 - 1.90 ) | 0.991 |
| med_CKDYes | 0.06 ( -1.05 - 1.17 ) | 1.06 ( 0.35 - 3.23 ) | 0.880 |
| history_med_MIYes | 0.20 ( -0.25 - 0.66 ) | 1.23 ( 0.78 - 1.93 ) | 0.205 |
| CMR_LVEF_5 | 0.18 ( -0.01 - 0.37 ) | 1.20 ( 0.99 - 1.45 ) | 0.007 |
| CMR_LGE_ischemic_extent_count | 0.24 ( 0.11 - 0.36 ) | 1.27 ( 1.12 - 1.43 ) | <0.001 |
| CMR_LGE_ischemic_transmurality_binB_non_viable | 0.49 ( -0.05 - 1.03 ) | 1.63 ( 0.95 - 2.80 ) | 0.012 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 0.84 ( -0.05 - 1.72 ) | 2.31 ( 0.95 - 5.60 ) | 0.008 |
| CMR_LGE_ischemic_location_4D_Septal | 2.18 ( 1.51 - 2.84 ) | 8.82 ( 4.54 - 17.14 ) | <0.001 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.71 ( 0.26 - 1.16 ) | 2.03 ( 1.29 - 3.20 ) | <0.001 |
| outcome_revascularisation_90daysYes | 0.48 ( -0.00 - 0.96 ) | 1.62 ( 1.00 - 2.62 ) | 0.005 |
## MODELS MULTI NESTED
var_base = c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5")
### Nested 4
table <- uni_multi_function(
df = df,
event = "event",
time = "time",
var_base = var_base,
var_added = c("CMR_LGE_ischemic_extent_count", "CMR_LGE_ischemic_transmurality_bin","CMR_LGE_ischemic_location_4",
"CMR_LGE_midwall_presence", "outcome_revascularisation_90days"))
# export in html
knitr::kable(table, caption = "Model full - MACE granularity (MACE = 797)", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Variable | Univariate_Analysis | model_multi_1 | model_multi_2 | model_multi_3 | model_multi_4 | model_multi_5 | model_multi_6 |
|---|---|---|---|---|---|---|---|
| demo_age | 1.04 (1.02-1.06), p=0.000 | 1.04 (1.02-1.06), p=0.000 | 1.02 (1.00-1.04), p=0.005 | 1.02 (1.00-1.04), p=0.014 | 1.02 (1.00-1.04), p=0.016 | 1.01 (0.99-1.04), p=0.064 | 1.02 (1.00-1.05), p=0.007 |
| demo_genderYes | 1.63 (0.96-2.78), p=0.010 | 1.67 (0.97-2.89), p=0.008 | 1.35 (0.78-2.35), p=0.127 | 1.35 (0.77-2.35), p=0.130 | 1.15 (0.65-2.02), p=0.495 | 1.13 (0.64-1.99), p=0.558 | 1.18 (0.67-2.10), p=0.412 |
| demo_BMI | 0.99 (0.96-1.03), p=0.496 | 0.99 (0.95-1.03), p=0.412 | 0.99 (0.95-1.04), p=0.664 | 0.99 (0.95-1.03), p=0.566 | 1.00 (0.95-1.04), p=0.784 | 0.99 (0.95-1.03), p=0.499 | 0.99 (0.95-1.03), p=0.414 |
| CV_risk_diabeteYes | 1.74 (1.18-2.59), p=0.000 | 1.59 (1.04-2.43), p=0.002 | 1.28 (0.84-1.95), p=0.096 | 1.27 (0.83-1.94), p=0.110 | 1.17 (0.77-1.79), p=0.290 | 1.19 (0.78-1.81), p=0.246 | 1.21 (0.80-1.85), p=0.193 |
| CV_risk_SmokingYes | 0.96 (0.57-1.62), p=0.843 | 1.27 (0.73-2.20), p=0.221 | 1.24 (0.71-2.17), p=0.280 | 1.19 (0.68-2.09), p=0.372 | 1.23 (0.70-2.15), p=0.310 | 1.24 (0.70-2.18), p=0.295 | 1.27 (0.72-2.24), p=0.243 |
| CV_risk_dyslipidemiaYes | 1.27 (0.85-1.88), p=0.092 | 1.17 (0.78-1.75), p=0.277 | 1.14 (0.75-1.74), p=0.366 | 1.17 (0.77-1.78), p=0.297 | 1.24 (0.81-1.91), p=0.159 | 1.31 (0.85-2.02), p=0.075 | 1.34 (0.87-2.05), p=0.057 |
| history_hospit_HFYes | 1.51 (0.79-2.88), p=0.075 | 1.13 (0.57-2.23), p=0.623 | 1.01 (0.49-2.08), p=0.969 | 0.95 (0.46-1.95), p=0.829 | 1.03 (0.49-2.16), p=0.922 | 1.13 (0.54-2.38), p=0.639 | 1.13 (0.54-2.37), p=0.636 |
| history_AFibYes | 1.80 (1.02-3.20), p=0.004 | 1.13 (0.62-2.08), p=0.563 | 0.96 (0.51-1.83), p=0.864 | 0.92 (0.48-1.75), p=0.709 | 0.98 (0.51-1.87), p=0.931 | 1.03 (0.54-1.96), p=0.891 | 1.00 (0.53-1.90), p=0.991 |
| med_CKDYes | 1.95 (0.71-5.38), p=0.064 | 1.16 (0.41-3.30), p=0.691 | 1.52 (0.50-4.60), p=0.290 | 1.49 (0.49-4.50), p=0.316 | 0.96 (0.31-2.95), p=0.920 | 1.00 (0.33-3.06), p=0.990 | 1.06 (0.35-3.23), p=0.880 |
| history_med_MIYes | 0.91 (0.60-1.37), p=0.517 | 0.92 (0.60-1.41), p=0.574 | 1.26 (0.81-1.96), p=0.147 | 1.37 (0.87-2.14), p=0.051 | 1.31 (0.84-2.06), p=0.089 | 1.18 (0.75-1.85), p=0.317 | 1.23 (0.78-1.93), p=0.205 |
| CMR_LVEF_5 | 0.91 (0.78-1.05), p=0.060 | 0.97 (0.83-1.14), p=0.646 | 1.02 (0.86-1.22), p=0.700 | 1.03 (0.86-1.23), p=0.661 | 1.13 (0.94-1.37), p=0.063 | 1.17 (0.97-1.41), p=0.020 | 1.20 (0.99-1.45), p=0.007 |
| CMR_LGE_ischemic_extent_count | 1.71 (1.56-1.88), p=0.000 | NA | 1.66 (1.49-1.83), p=0.000 | 1.61 (1.45-1.79), p=0.000 | 1.25 (1.10-1.41), p=0.000 | 1.24 (1.10-1.40), p=0.000 | 1.27 (1.12-1.43), p=0.000 |
| CMR_LGE_ischemic_transmurality_binB_non_viable | 3.46 (2.10-5.70), p=0.000 | NA | NA | 2.21 (1.32-3.71), p=0.000 | 1.55 (0.91-2.64), p=0.022 | 1.47 (0.86-2.51), p=0.045 | 1.63 (0.95-2.80), p=0.012 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 4.14 (1.84-9.35), p=0.000 | NA | NA | NA | 3.10 (1.34-7.17), p=0.000 | 2.46 (1.03-5.92), p=0.004 | 2.31 (0.95-5.60), p=0.008 |
| CMR_LGE_ischemic_location_4D_Septal | 18.04 (10.44-31.16), p=0.000 | NA | NA | NA | 8.81 (4.58-16.98), p=0.000 | 8.07 (4.17-15.62), p=0.000 | 8.82 (4.54-17.14), p=0.000 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 4.01 (2.65-6.07), p=0.000 | NA | NA | NA | NA | 1.91 (1.22-3.00), p=0.000 | 2.03 (1.29-3.20), p=0.000 |
| outcome_revascularisation_90daysYes | 0.37 (0.25-0.55), p=0.000 | NA | NA | NA | NA | NA | 1.62 (1.00-2.62), p=0.005 |
# Export in word
ft <- flextable(table)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Model 4 - MACE with LGE pop (794)", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab22-multi_tradi-MACE_LGEpop-MACE-",Sys.Date(), ".docx")))Tab23-incr-MACE_LGEpop-MACE
var1 <- c("demo_age", "demo_gender" , "demo_BMI" , "CV_risk_diabete" , "CV_risk_Smoking" , "CV_risk_dyslipidemia" , "history_hospit_HF", "history_AFib" , "med_CKD" , "history_med_MI" , "CMR_LVEF_5")
var2 <- c(var1, "CMR_LGE_ischemic_extent_count")
var3A <- c(var2, "CMR_LGE_ischemic_transmurality_bin")
var3B <- c(var2, "CMR_LGE_ischemic_location_4")
var3C <- c(var2, "CMR_LGE_midwall_presence")
var4 <- c(var2, "CMR_LGE_ischemic_transmurality_bin", "CMR_LGE_ischemic_location_4", "CMR_LGE_midwall_presence")
df_selected <- df_MACE %>% filter(
CMR_LGE_ischemic_presence=="Presence_of_ischemic_LGE") %>% mutate(
time = time.censored,
event = outcome_MACE,
CMR_LVEF_5 = CMR_LVEF/5,
CMR_LGE_ischemic_transmurality_bin = case_when(
CMR_LGE_ischemic_transmurality == "B_Subendocardial<50%" ~ "A_viable",
CMR_LGE_ischemic_transmurality != "B_Subendocardial<50%" ~ "B_non_viable",
TRUE ~ NA)
) %>% droplevels()
# MODEL 1
results.model1 <- CoxPredictScore(derivation = df_selected, validation = df_selected,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var1,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model1$model_stats## NULLresults.model1$derivation$auc## [1] "0.66 [0.6-0.72]"# MODEL 2
results.model2 <- CoxPredictScore(derivation = df_selected, validation = df_selected,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var2,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model2$model_stats## NULLresults.model2$derivation$auc## [1] "0.83 [0.77-0.86]"roc.test(results.model1$roc_derivation$roc_obj, results.model2$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model1$roc_derivation$roc_obj and results.model2$roc_derivation$roc_obj
## Z = -6.4573, p-value = 1.066e-10
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.2116426 -0.1130805
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.6644607 0.8268223# MODEL 3A
results.model3A <- CoxPredictScore(derivation = df_selected, validation = df_selected,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3A,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3A$model_stats## NULLresults.model3A$derivation$auc## [1] "0.83 [0.78-0.87]"roc.test(results.model2$roc_derivation$roc_obj, results.model3A$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3A$roc_derivation$roc_obj
## Z = -0.32169, p-value = 0.7477
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.01749672 0.01256302
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8268223 0.8292891# MODEL 3B
results.model3B <- CoxPredictScore(derivation = df_selected, validation = df_selected,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3B,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3B$model_stats## NULLresults.model3B$derivation$auc## [1] "0.86 [0.82-0.91]"roc.test(results.model2$roc_derivation$roc_obj, results.model3B$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3B$roc_derivation$roc_obj
## Z = -2.5217, p-value = 0.01168
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.066648317 -0.008353462
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8268223 0.8643232# MODEL 3C
results.model3C <- CoxPredictScore(derivation = df_selected, validation = df_selected,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var3C,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model3C$model_stats## NULLresults.model3C$derivation$auc## [1] "0.84 [0.8-0.88]"roc.test(results.model2$roc_derivation$roc_obj, results.model3C$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model3C$roc_derivation$roc_obj
## Z = -2.1886, p-value = 0.02862
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.03111324 -0.00171486
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8268223 0.8432363# MODEL 4
results.model4 <- CoxPredictScore(derivation = df_selected, validation = df_selected,
time.name = "time",
times = 10*12,
event.name ="event",
variables = var4,
n_bootstrap = 100,
ci_level = 0.995,
seed = 123)
results.model4$model_stats## NULLresults.model4$derivation$auc## [1] "0.87 [0.83-0.91]"roc.test(results.model2$roc_derivation$roc_obj, results.model4$roc_derivation$roc_obj)##
## DeLong's test for two correlated ROC curves
##
## data: results.model2$roc_derivation$roc_obj and results.model4$roc_derivation$roc_obj
## Z = -2.6925, p-value = 0.007091
## alternative hypothesis: true difference in AUC is not equal to 0
## 95 percent confidence interval:
## -0.07475831 -0.01177126
## sample estimates:
## AUC of roc1 AUC of roc2
## 0.8268223 0.8700871Score using all LGE population
Tab24-score_development
# 1) Coxph function
df <- df_LGE %>% droplevels()
# Function
# Assuming model is already correctly fitted as shown:
model <- coxph(formula = Surv(outcome_FU_time_death, outcome_death) ~ CMR_LGE_ischemic_extent_count + CMR_LGE_ischemic_transmurality + CMR_LGE_ischemic_location_4 + CMR_LGE_midwall_presence, data = df)
# Function
data <- extract_model_stats(model, conf_level = 0.995, decimals = 2)
# Additional transformations as per your code (multiplying coefficients by 3, etc.)
data <- data %>%
mutate(
# Extract the coefficient and convert to numeric using gsub to remove non-numeric parts
coefficients = as.numeric(gsub(" .*", "", Coefficients_CI)),
# Multiply the extracted coefficient by 3
coeff_x3 = coefficients * 3,
# Round the result of the multiplication
score = round(coeff_x3)
)
knitr::kable(data, caption = "Score", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| Coefficients_CI | HR_CI | p_value | coefficients | coeff_x3 | score | |
|---|---|---|---|---|---|---|
| CMR_LGE_ischemic_extent_count | 0.32 ( 0.25 - 0.39 ) | 1.38 ( 1.28 - 1.48 ) | <0.001 | 0.32 | 0.96 | 1 |
| CMR_LGE_ischemic_transmuralityC_Subendocardial≥50% | 0.82 ( 0.45 - 1.19 ) | 2.28 ( 1.58 - 3.28 ) | <0.001 | 0.82 | 2.46 | 2 |
| CMR_LGE_ischemic_transmuralityD_Transmural | 1.15 ( 0.75 - 1.55 ) | 3.16 ( 2.12 - 4.73 ) | <0.001 | 1.15 | 3.45 | 3 |
| CMR_LGE_ischemic_location_4C_Anterior_without_septal | 1.60 ( 1.20 - 2.00 ) | 4.95 ( 3.31 - 7.41 ) | <0.001 | 1.60 | 4.80 | 5 |
| CMR_LGE_ischemic_location_4D_Septal | 2.06 ( 1.70 - 2.43 ) | 7.87 ( 5.45 - 11.35 ) | <0.001 | 2.06 | 6.18 | 6 |
| CMR_LGE_midwall_presenceB_Presence_of_midwall_LGE | 0.63 ( 0.35 - 0.92 ) | 1.88 ( 1.41 - 2.51 ) | <0.001 | 0.63 | 1.89 | 2 |
# Create a new Word document
write.csv(x = data, file = here(tables_output_dir,"Tab24-score_development.csv"), col.names = TRUE)
# 3) Find best cut-off
fit = partykit::ctree(Surv(outcome_FU_time_death/12, outcome_death) ~ score, data = df, maxdepth = 2)
plot(fit,
main = "Decision tree for score on derivation (N=2,900)",
digits = 2, cex = 0.2, branch.type = "square",
tp_args = list(pval = TRUE),
gp_args = list(fontsize = 2))
## Fig14-Schoenfelds_residuals_score
fit <- coxph(formula = Surv(outcome_FU_time_death, outcome_death) ~ score, data = df_LGE)
model <- cox.zph(fit)
plot <- ggcoxzph(model)[1]
plot## $`1`
# Save each plot
ggsave(
filename = here(figures_output_dir, paste0("Fig14-Schoenfelds_residuals_score-",Sys.Date(),".png")),
plot = plot[[1]],
width = 10,
height = 6,
units = "in",
dpi = 300
)Mortality
Fig15-Score_mortality_spline WILSON
library(binom)
# Assuming df_LGE is your dataframe
data <- df_LGE %>%
droplevels() %>%
mutate(
event = outcome_death,
time = outcome_FU_time_death
)
# Summarizing data and calculating Wilson score intervals
D <- data %>%
group_by(score) %>%
summarise(
n = n(),
events_sum = sum(event), # Sum of events per group
mortality = events_sum / n, # Proportion of events
survival = 1 - mortality,
std_error = sd(event, na.rm = TRUE) / sqrt(n), # Standard error of event proportion
col = unique(score_categ)
) %>%
mutate(
wilson = binom.confint(events_sum, n, methods = "wilson", conf.level = 0.995) # Calculating Wilson score intervals
)
# Extracting lower and upper bounds from Wilson score
D <- D %>%
mutate(
lower_95 = wilson[, "lower"],
upper_95 = wilson[, "upper"]
)
# For the graphics
D[16:17, "upper_95"] <- 1.01
# Continue with your existing plotting code
color_palette <- c("1_low_risk" = "green4", "2_medium_risk" = "orange2", "3_high_risk" = "red4")
max_n <- max(D$n)
scale_factor <- max_n / max(D$mortality, na.rm = TRUE)
# Smooth predictions and confidence intervals using loess model
D$smooth_pred <- predict(loess(mortality * max_n ~ score, data = D), se = FALSE)
D$smooth_lower <- predict(loess(lower_95 * max_n ~ score, data = D), se = FALSE)
D$smooth_upper <- predict(loess(upper_95 * max_n ~ score, data = D), se = FALSE)
# Base plot with mortality predictions
gggraph <- ggplot(D, aes(x = score, y = mortality * max_n)) +
geom_bar(stat = "identity", aes(y = n, fill = col), position = position_dodge(), width = 0.9) +
scale_fill_manual(values = color_palette) +
geom_ribbon(aes(ymin = smooth_lower, ymax = smooth_upper), fill = "#F37B59", alpha = 1) +
geom_line(aes(y = smooth_pred), color = "red", size = 1.5) +
scale_y_continuous(
name = "Frequency (N)",
sec.axis = sec_axis(~ . / max(D$n), name = "All-cause mortality rate (%)"),
limits = c(-0.1 * max_n, 1.3 * max_n)
) +
labs(
title = "The CMR-LGE score (N=3,591)",
x = "CMR-LGE score",
y = "Frequency (N)"
) +
xlim(0, max(D$score)) +
scale_x_continuous(breaks = 1:17) +
theme_classic(base_size = 14) +
theme(
plot.background = element_rect(fill = "white", color = NA),
panel.background = element_rect(fill = "white", color = NA),
legend.position = "none",
axis.line = element_line(color = "black"),
axis.text = element_text(color = "black"),
axis.title = element_text(color = "black")
)
# Print the plot
print(gggraph)
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig15-Score_mortality_spline", Sys.Date(),".png")),
plot = gggraph, width = 9, height = 6, units = "in", dpi = 600)Tab25-Mortality prediction at 5 and 10 years (w/bootstrap)
library(survival)
library(dplyr)
library(purrr) # for the map functions
library(kableExtra)
# Main analysis
n_bootstrap <- 100
time.choosen <- 10*12 # Predicting at 5 and 10 years
# Assuming df_LGE is your dataframe
data <- df_LGE %>%
droplevels() %>%
mutate(
event = outcome_death,
time = outcome_FU_time_death
)
# Function to process data for survival analysis
process_data <- function(data, event_name, time_name, time_threshold) {
data$event_time <- ifelse(data[[event_name]] == 1 & data[[time_name]] < time_threshold, 1,
ifelse(data[[time_name]] >= time_threshold, 0, NA))
data <- filter(data, !is.na(event_time))
return(data)
}
# Preprocess the dataset
data_processed <- process_data(data, event_name = "event", time_name = "time", time_threshold = time.choosen)
# Fitting a Cox model
model.cox <- coxph(Surv(time, event_time) ~ score, data = data_processed, x = TRUE)
# Function to generate predictions for bootstrap samples
predictions_function <- function(data, model, times) {
indices <- sample(1:nrow(data), replace = TRUE)
bootstrap_sample <- data[indices, ]
predictions <- 1 - predictSurvProb(model, newdata = bootstrap_sample, times = c(times))
# Return a dataframe with predictions and the associated scores
data.frame(score = bootstrap_sample$score, predictions = predictions)
}
# Assuming `data_processed` is your pre-processed dataset and `model.cox` is your fitted Cox model
n_bootstrap <- 100 # Number of bootstrap samples
time.choosen <- 5 * 12 # Predicting at 5 years, can add more times if needed
# Apply bootstrap to calculate predictions for each iteration
bootstrap_results <- purrr::map(1:n_bootstrap, ~predictions_function(data_processed, model.cox, time.choosen))
# Combine all bootstrap results into a single dataframe
bootstrap_data <- do.call(rbind, bootstrap_results)
# Group by score and summarize the predictions in a list or any other summary statistic
summary_data.5 <- bootstrap_data %>%
group_by(score) %>%
summarise(
mean_predictions.5 = mean(predictions, na.rm = TRUE)
)
# Assuming `data_processed` is your pre-processed dataset and `model.cox` is your fitted Cox model
n_bootstrap <- 100 # Number of bootstrap samples
time.choosen <- 10 * 12 # Predicting at 5 years, can add more times if needed
# Apply bootstrap to calculate predictions for each iteration
bootstrap_results <- purrr::map(1:n_bootstrap, ~predictions_function(data_processed, model.cox, time.choosen))
# Combine all bootstrap results into a single dataframe
bootstrap_data <- do.call(rbind, bootstrap_results)
# Group by score and summarize the predictions in a list or any other summary statistic
summary_data.10 <- bootstrap_data %>%
group_by(score) %>%
summarise(
mean_predictions.10 = mean(predictions, na.rm = TRUE)
)
summary_data <- cbind(summary_data.5, summary_data.10)
df_data <- summary_data[,c(1,2,4)]
df_data## score mean_predictions.5 mean_predictions.10
## 1 1 0.009828916 0.02497375
## 2 2 0.013158908 0.03334758
## 3 3 0.017607013 0.04446451
## 4 4 0.023540680 0.05917241
## 5 5 0.031441791 0.07854167
## 6 6 0.041937296 0.10389230
## 7 7 0.055833964 0.13679737
## 8 8 0.074154173 0.17903594
## 9 9 0.098165683 0.23245372
## 10 10 0.129391645 0.29867435
## 11 11 0.169576689 0.37860040
## 12 12 0.220570562 0.47167573
## 13 13 0.284075033 0.57499272
## 14 14 0.361191961 0.68255994
## 15 15 0.451732703 0.78536305
## 16 16 0.553340864 0.87300586
## 17 17 0.660686345 0.93717522write.csv(df_data, file = "outputs/csv_files/pred_by_score.csv")
# export in html
knitr::kable(df_data, caption = "Prediction by the score (Tab25)", format = "html") %>%
kableExtra::kable_styling(full_width = F, bootstrap_options = c("striped", "hover", "condensed", "bordered"))| score | mean_predictions.5 | mean_predictions.10 |
|---|---|---|
| 1 | 0.0098289 | 0.0249738 |
| 2 | 0.0131589 | 0.0333476 |
| 3 | 0.0176070 | 0.0444645 |
| 4 | 0.0235407 | 0.0591724 |
| 5 | 0.0314418 | 0.0785417 |
| 6 | 0.0419373 | 0.1038923 |
| 7 | 0.0558340 | 0.1367974 |
| 8 | 0.0741542 | 0.1790359 |
| 9 | 0.0981657 | 0.2324537 |
| 10 | 0.1293916 | 0.2986743 |
| 11 | 0.1695767 | 0.3786004 |
| 12 | 0.2205706 | 0.4716757 |
| 13 | 0.2840750 | 0.5749927 |
| 14 | 0.3611920 | 0.6825599 |
| 15 | 0.4517327 | 0.7853630 |
| 16 | 0.5533409 | 0.8730059 |
| 17 | 0.6606863 | 0.9371752 |
# Export in word
ft <- flextable(df_data)
ft <- set_table_properties(ft, layout = "autofit")
doc <- read_docx() %>%
body_add_par("Prediction by the score (Tab25)", style = "heading 1") %>%
body_add_flextable(ft)
print(doc, target = here(tables_output_dir,paste0("Tab25-Predictions_Score-",Sys.Date(), ".docx")))Fig16-Score_mortality_stacked
data <- df_LGE %>%
droplevels() %>% mutate(
event = outcome_death,
time = outcome_FU_time_death
)
# Assuming df contains your data and D contains the summarized results
D <- data %>%
group_by(score) %>%
summarise(
n = n(),
events_sum = sum(event), # Sum of events per group
mortality = events_sum / n, # Proportion of events
survival = 1-mortality,
std_error = sd(event) / sqrt(n), # Standard error of event proportion
lower_95 = mortality - 1.96 * std_error,
upper_95 = mortality + 1.96 * std_error,
col = unique(score_categ)
)
color_palette <- c("1_low_risk" = "green4", "2_medium_risk" = "orange2", "3_high_risk" = "red4")
# the dataframe
D <- as.data.frame(D)
# Reshape the data for plotting
df_long <- D %>%
pivot_longer(cols = c("mortality", "survival"), names_to = "outcome", values_to = "proportion")
# Creating the 100% stacked bar plot
g <- ggplot(df_long, aes(x = factor(score), y = proportion, fill = outcome)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
labs(x = "Score", y = "Proportion of Outcome", fill = "Outcome") +
theme_classic() +
scale_fill_manual(values = c("mortality" = "red", "survival" = "green4")) # Customize colors as needed
g
ggsave(
filename = here(figures_output_dir, paste0("Fig16-Score_mortality_stacked-", Sys.Date(),"_plot.png")),
plot = g, width = 9, height = 6, units = "in", dpi = 600)Function for AUC and PRAUC (to add)
Fig17-Score_AUC10
# data <- df_LGE %>%
# droplevels()
#
# results.score.10 <- CoxPredictScore(
# derivation = data,
# validation = data,
# time.name = "outcome_FU_time_death",
# times = 10*12,
# event.name ="outcome_death",
# variable = "score",
# n_bootstrap = 100,
# ci_level = 0.995,
# seed = 123)
#
#
# # CREATING AUC CURVES
# roclist <- list(results.score.10$roc_derivation$roc_obj)
#
# names(roclist) <- c(paste0("AUC Score - ", results.score.10$derivation$auc ))
#
# library(pROC)
# # GRAPHCIS - AUC (Solenn)
# g <- ggroc(roclist, aes = "colour", legacy.axes = TRUE) + geom_line(size = 0.8) +
# theme_classic()+
# ggtitle("AUC 10 years") +
# labs(x = "1 - Specificity",
# y = "Sensitivity") +
# theme_minimal() +
# theme(
# plot.title = element_text(size = 14, hjust = 0.5),
# plot.subtitle = element_text(size = 12, hjust = 0.5),
# axis.line = element_line(color = "black"),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.x = element_text(size = 14), # Increase size of X axis labels
# axis.text.y = element_text(size = 14), # Increase size of Y axis labels
# axis.title.x = element_text(size = 16), # Increase size of X axis title
# axis.title.y = element_text(size = 16), # Increase size of Y axis title
# legend.text = element_text(size=14),
# legend.position = c(0.95, 0.05), # Adjust the position within the graph (x, y)
# legend.justification = c(1, 0), # Set the justification to bottom-right
# legend.box.just = "right", # Set the legend box justification to right
# legend.title = element_blank(), # Remove legend title
# legend.box.background = element_rect(color = "black", fill = NA)) + # Add black border
#
# scale_colour_manual(values = c( "red"))
#
#
# # Add the y = x line (line of equality)
# g <- g + geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black")
#
# g
#
# ggsave(
# filename = here(figures_output_dir, paste0("Fig17-Score_AUC10-", Sys.Date(),".png")),
# plot = g, width = 9, height = 6, units = "in", dpi = 600)Fig18-Score_PRAUC10
# data <- df_LGE %>%
# droplevels()
#
# results.score.10 <- CoxPredictScore(
# derivation = data,
# validation = data,
# time.name = "outcome_FU_time_death",
# times = 10*12,
# event.name ="outcome_death",
# variable = "score",
# n_bootstrap = 100,
# ci_level = 0.995,
# seed = 123)
#
# perc_death <- 379/1790
#
#
# g <- ggplot() +
# geom_line(data = results.score.10$roc_derivation$coords, aes(x = recall, y = precision, color = "A_Score")) +
# geom_hline(yintercept = perc_death, color = "black", linetype = "dashed") +
# labs(
# x = "Recall",
# y = "Precision",
# title = "PRAUC 10",
# color = "Dataset"
# ) +
# scale_color_manual(
# values = c("A_Score" = "red"),
# labels = c(paste("Cox Score - ", results.score.10$derivation$prauc)))+
# theme_classic () +
# theme(
# plot.title = element_text(size = 14, hjust = 0.5),
# plot.subtitle = element_text(size = 12, hjust = 0.5),
# axis.line = element_line(color = "black"),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.x = element_text(size = 14), # Increase size of X axis labels
# axis.text.y = element_text(size = 14), # Increase size of Y axis labels
# axis.title.x = element_text(size = 16), # Increase size of X axis title
# axis.title.y = element_text(size = 16), # Increase size of Y axis title
# legend.text = element_text(size=14),
# legend.position = c(0.9, 0.05),
# legend.justification = c(1, 0),
# legend.box.just = "right", # Set the legend box justification to right
# legend.title = element_blank(), # Remove legend title
# legend.box.background = element_rect(color = "black", fill = NA),
# plot.margin = margin(20, 20, 20, 20),
# plot.background = element_rect(fill = "white", color = "white", size = 1)) + # Add a white background
#
# scale_y_continuous(limits = c(0, 1)) # Set y-axis limits to 0 and 1
#
# g
#
# ggsave(
# filename = here(figures_output_dir, paste0("Fig18-Score_PRAUC10-", Sys.Date(),".png")),
# plot = g, width = 6, height = 6, units = "in", dpi = 600)Fig19-LGE-KM-score-death
df_selected <- df_LGE %>% mutate(
event = outcome_death,
time = outcome_FU_time_death
)
# Data parameters
results <- createSurvivalPlot(
data = df_LGE,
compared_with = "score_categ",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("green4","orange2","red4"),
my_legends = c("Low","Medium", "High"),
confint_choosen = 0.995,
mytitle = c("Fig19-LGE-KM-score-death")
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ score_categ"results$HR## HR_CI p_value
## score_categ2_medium_risk 9.08 ( 5.98 - 13.78 ) <0.001
## score_categ3_high_risk 31.39 ( 22.14 - 44.49 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig19-LGE-KM-score-death_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig19-LGE-KM-score-death_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Fig20-LGE-KM-score-MACE
###### UNIVARIATE
df_selected <- df_MACE %>% mutate(
event = outcome_MACE,
time = time.censored # method of censoring when poatients die from another cause.
)
# Data parameters
results <- createSurvivalPlot(
data = df_selected,
compared_with = "score_categ",
time = "outcome_FU_time_death",
event = "outcome_death",
my_colors = c("green4","orange2","red4"),
my_legends = c("Low","Medium", "High"),
confint_choosen = 0.995,
mytitle = c("Fig20-LGE-KM-score-MACE")
)## [1] "Cox model formula: Surv(outcome_FU_time_death/12, outcome_death) ~ score_categ"results$HR## HR_CI p_value
## score_categ2_medium_risk 7.35 ( 3.68 - 14.66 ) <0.001
## score_categ3_high_risk 25.55 ( 16.27 - 40.12 ) <0.001results$ggsurv
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig20-LGE-KM-score-MACE_plot-", Sys.Date(),".png")),
plot = results$ggsurv$plot, width = 10, height = 6, units = "in", dpi = 600)
ggsave(
filename = here(figures_output_dir,paste0("Fig20-LGE-KM-score-MACE_risk-", Sys.Date(),".png")),
plot = results$ggsurv$table, width = 10, height = 3, units = "in", dpi = 600)Other figures
Fig21-Csv_ARP_plot
exported in csv to build the graphics on excell
# 1] extent
py <- pyears(Surv(outcome_FU_time_death*(365/12), outcome_death) ~ CMR_LGE_ischemic_extent_categ, data = df_LGE, data.frame = T)
df_ext<<- py$data
colnames(df_ext)[1] <- "subgroups"
df_ext$group <- "B_Ext"
df_ext$annual_rates <- round(100*(df_ext$event/df_ext$pyears), 1) # Ca fait du 100 personnes années
# 2] TRANSMURALITY
py <- pyears(Surv(outcome_FU_time_death*(365/12), outcome_death) ~ CMR_LGE_ischemic_transmurality, data = df_LGE, data.frame = T)
df_transm<<- py$data
colnames(df_transm)[1] <- "subgroups"
df_transm$group <- "C_Transm"
df_transm$annual_rates <- round(100*(df_transm$event/df_transm$pyears), 1) # Ca fait du 100 personnes années
# 3] MULTIPLE
py <- pyears(Surv(outcome_FU_time_death*(365/12), outcome_death) ~ CMR_LGE_ischemic_multiple, data = df_LGE, data.frame = T)
df_multi<<- py$data
colnames(df_multi)[1] <- "subgroups"
df_multi[1,1] <- "A_Focal"
df_multi[2,1] <- "B_Multi"
df_multi$group <- "D_Multiple"
df_multi$annual_rates <- round(100*(df_multi$event/df_multi$pyears), 1) # Ca fait du 100 personnes années
# 4] SEPTAL
py <- pyears(Surv(outcome_FU_time_death*(365/12), outcome_death) ~ CMR_LGE_ischemic_location_4, data = df_LGE, data.frame = T)
df_septal <- py$data
colnames(df_septal)[1] <- "subgroups"
df_septal[1,1] <- "A_No_septal"
df_septal[2,1] <- "B_Anterior"
df_septal[3,1] <- "C_Septal"
df_septal$group <- "E_localization"
df_septal$annual_rates <- round(100*(df_septal$event/df_septal$pyears), 1) # Ca fait du 100 personnes années
# 5] MIDWALL
py <- pyears(Surv(outcome_FU_time_death*(365/12), outcome_death) ~ CMR_LGE_midwall_presence, data = df_LGE, data.frame = T)
df_mid<<- py$data
colnames(df_mid)[1] <- "subgroups"
df_mid[1,1] <- "A_No_midwall_ass"
df_mid[2,1] <- "B_Midwall_ass"
df_mid$group <- "F_Midwall"
df_mid## subgroups pyears n event group
## 1 <NA> 28911.364 3237 418 F_Midwall
## 2 <NA> 2923.997 354 131 F_Midwalldf_mid$annual_rates <- round(100*(df_mid$event/df_mid$pyears), 1) # Ca fait du 100 personnes années
# GLOBAL DF
df_global <- rbind(df_ext, df_transm, df_multi, df_septal ,df_mid) #df_normal
df_global <- df_global %>% mutate(subgroups = paste(as.character(group), "~", subgroups,sep=""))
# Create the grouped barplot using ggplot2
library(ggplot2)
# Create the plot
gg_graph <- ggplot(df_global, aes(x = group, y = annual_rates, fill = subgroups, label = annual_rates)) +
geom_bar(position = "dodge", stat = "identity", just = 1, width = 0.7) + # Equal-width bars
scale_fill_manual(values = adjustcolor(c("#4292C6","#D52B2B","#2C3E50","#4292C6","#D52B2B","#2C3E50", "#D52B2B", "#2C3E50","#4292C6","#D52B2B","#2C3E50","#D52B2B","#2C3E50"), alpha.f = 0.8)) + # Set custom colors
labs(title = "Grouped Barplot with Subgroups",
x = "group", # X-axis label
y = "Annual Rates", # Y-axis label
fill = "subgroups") + # Legend title
geom_text(
aes(x = group, y = annual_rates, label = sprintf("%.1f", annual_rates), group = subgroups),
position = position_dodge(width = 1),
vjust = 0, size = 4, fontface = "bold") +
scale_y_continuous(expand = c(0, 1), limits = c(0,15), breaks = seq(0, 17.5, 2.5)) +
theme_bw()+
theme(
panel.grid.major = element_line(color = "gray", size = 0.2, linetype = "dotted"),
panel.grid.minor = element_line(color = "gray", size = 0.2, linetype = "dotted"),
plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
# axis.text.x = element_blank(), to remove the x axis legend
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 12, face = "bold"),
panel.border = element_blank(),
legend.position = "none", # Move the legend to the top-left corner
legend.justification = c(0, 1), # Anchor the legend to the top-left corner
legend.text = element_text(size = 12), # Adjust the legend text size
legend.title = element_text(size = 14) # Adjust the legend title size
)
gg_graph <- gg_graph + coord_flip()
plot.new()
print(gg_graph,newpage = FALSE)
# finaly details done using excell
write.csv(df_global, file = here(figures_output_dir,"Fig21-annualized_death_rate.csv"))
# Compare subgroups
print(df_global) # u see the dataframe used to build the graph## subgroups pyears n event group
## 1 B_Ext~B_1_2_segments 12538.579 1392 23 B_Ext
## 2 B_Ext~C_3_5_segments 17452.546 1953 294 B_Ext
## 3 B_Ext~D_more6_segments 1844.237 246 232 B_Ext
## 4 C_Transm~B_Subendocardial<50% 15267.793 1698 79 C_Transm
## 5 C_Transm~C_Subendocardial≥50% 13756.994 1549 259 C_Transm
## 6 C_Transm~D_Transmural 2810.575 344 211 C_Transm
## 7 D_Multiple~NA 28906.618 3226 319 D_Multiple
## 8 D_Multiple~NA 2928.744 365 230 D_Multiple
## 9 E_localization~NA 24220.411 2662 99 E_localization
## 10 E_localization~NA 3632.179 422 105 E_localization
## 11 E_localization~NA 3982.772 507 345 E_localization
## 12 F_Midwall~NA 28911.364 3237 418 F_Midwall
## 13 F_Midwall~NA 2923.997 354 131 F_Midwall
## annual_rates
## 1 0.2
## 2 1.7
## 3 12.6
## 4 0.5
## 5 1.9
## 6 7.5
## 7 1.1
## 8 7.9
## 9 0.4
## 10 2.9
## 11 8.7
## 12 1.4
## 13 4.5n = 1 # now i want to compare by 2 therefore i use this method
rate_comp <- ratedifference(df_global$event[n + 1], df_global$event[n], df_global$pyears[n + 1], df_global$pyears[n], CRC = TRUE, conf.level = 0.995)## Cases Person-time Incidence rates Lower CL Upper CL
## Exposed 2.940000e+02 1.745255e+04 1.684568e-02 1.408788e-02 1.960348e-02
## Unexposed 2.300000e+01 1.253858e+04 1.834339e-03 7.606874e-04 2.907990e-03
## Total 3.170000e+02 2.999112e+04 1.056979e-02 8.903373e-03 1.223621e-02value <- as.character(df_global$p.value)
result <- ifelse(df_global$p.value < 0.001 | df_global$p.value == 0, "<0.001", value)
print(result)## logical(0)Fig22-LGE-Forestplot
library(forestplot)
# function
df_selected <- df_all %>%
mutate(
demo_age_categ = factor(case_when(
demo_age < 65 ~ "age<65",
TRUE ~ "age≥65"
), levels = c("age<65", "age≥65")),
CMR_LVEF_categ = factor(ifelse(
CMR_LVEF <= 40, "HFrEF", "HFmrEF"
), levels = c("HFrEF", "HFmrEF"))
)
print("Forest plot")## [1] "Forest plot"D <- forest_function(
df = df_selected,
time_var = "outcome_FU_time_death",
event_var = "outcome_death",
var_of_interest = "CMR_LGE_ischemic_presence",
"demo_age_categ",
"demo_gender",
"CV_risk_obesity",
"CV_risk_diabete",
"CV_risk_HTA",
"CV_risk_Smoking",
"clini_NYHA",
"CMR_LVEF_categ"
)
base_data <- D
name_row_vector <- c("Age","<65 years", "≥65 years", "Gender","Female", "Male", "Body Mass Index ","No obesity", "Obesity", "Diabete mellitus", "No", "Yes", "Hypertension", "No", "Yes", "Smoking", "No", "Yes", "Symptoms", "Asymptomatic","Symptomatic", "LVEF","≤40%", ">40%")
base_data$name <- name_row_vector
# Generate the forestplot
forest_plot <- base_data |>
forestplot(
labeltext = c(name, ratio1, ratio2, OR, Pval, PValInter),
graph.pos = 4,
mean = mean,
upper = upper,
lower = lower,
is.summary = FALSE, # c(rep(TRUE, 2), rep(c(TRUE, FALSE, FALSE), 8)), #Make Text Bold to highlight Summary rows
boxsize = 0.2, # Adjust the boxsize as needed)
clip = c(0, 7), #Lower and upper limits for clipping confidence intervals to arrows
align = c("r", "c", "c", "r","l", "l"), # make it work
vertices = TRUE,
xlog = FALSE, #Log scale on X axis
col = fpColors(box = "royalblue",
line = "darkblue",
summary = "royalblue")) |>
fp_set_zebra_style("#EFEFEF", ignore_subheaders = FALSE)|>
fp_set_style(box = "red",
line = "gray0",
summary = "royalblue",
align = "lrrr")
print(forest_plot)
Fig23-LGE-spline_curves
Encore des bug car HR de lui-même devrait être de 1 non ? Ref = 3
# Data
df_selected <- df_LGE
fit.cox = coxph(Surv(outcome_FU_time_death/12,outcome_death) ~ pspline(CMR_LGE_ischemic_extent_count), data = df_selected) # used median for 1 ...
## get predicted values for fitted spline
predicted = predict(fit.cox , type = "terms" , se.fit = TRUE , terms = 1, reference = "strata")
df_test <- data.frame(
LGE_count = df_selected$CMR_LGE_ischemic_extent_count,
HR = exp(predicted$fit),
Upper_CI = exp(predicted$fit + 2.81 * predicted$se),
Lower_CI = exp(predicted$fit - 2.81 * predicted$se)
)
colnames(df_test) <- c("LGE_count","HR","Upper_CI","Lower_CI")
df_test <- as.data.frame(df_test)
# Plot using ggplot2
library(ggplot2)
gggraph <- ggplot(aes(x = LGE_count, y = HR), data=df_test) +
geom_abline(slope = 0, intercept = 1, color = "black") +
geom_vline(xintercept = 3, linetype = "dashed", size = 0.5, color = "black")+
geom_smooth(method = "loess",color = "red", size = 0.7) +
geom_ribbon(aes(ymin = predict(loess(Upper_CI ~ LGE_count, data = df_test)),
ymax = predict(loess(Lower_CI ~ LGE_count, data = df_test))),
fill = "orangered", alpha = 0.3) +
labs(
title = "Fig23-LGE-spline_curves",
x = "Number of ischemic LGE segments",
y = expression("Hazard Ratio for all-cause mortality n\ vs 3 segments of LGE (99.5% CI)")) +
xlim(0, 8) +
ylim(0, 40) +
scale_y_continuous(breaks = c(1, 5, 10, 30)) + # Y-axis marks at 1, 5, 10, 30
scale_x_continuous(breaks = seq(0, 9, 1)) + # X-axis marks every 1
theme(
panel.background = element_rect(fill = "white", color = NA),
axis.line = element_line(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(size = 16), # Increase x-axis label font size
axis.text.y = element_text(size = 16)
)
gggraph
# Save the plot in a folder
ggsave(
filename = here(figures_output_dir, paste0("Fig23-LGE-spline_curves-", Sys.Date(), ".png")),
plot = gggraph, width = 9, height = 6, units = "in", dpi = 600)